Move and neighborhood selection

1. Move and neighborhood introduction

1.1. What is a Move?

A Move is a change (or set of changes) from a solution A to a solution B. For example, the move below changes queen C from row 0 to row 2:

singleMoveNQueens04

The new solution is called a neighbor of the original solution, because it can be reached in a single Move. Although a single move can change multiple queens, the neighbors of a solution should always be a very small subset of all possible solutions. For example, on that original solution, these are all possible changeMoves:

possibleMovesNQueens04

If we ignore the four changeMoves that have no impact and are therefore not doable, we can see that the number of moves is n * (n - 1) = 12. This is far less than the number of possible solutions, which is n ^ n = 256. As the problem scales out, the number of possible moves increases far less than the number of possible solutions.

Yet, in four changeMoves or less we can reach any solution. For example we can reach a very different solution in three changeMoves:

sequentialMovesNQueens04

There are many other types of moves besides changeMoves. Many move types are included out-of-the-box, but you can also implement custom moves.

A Move can affect multiple entities or even create/delete entities. But it must not change the problem facts.

All optimization algorithms use Moves to transition from one solution to a neighbor solution. Therefore, all the optimization algorithms are confronted with Move selection: the craft of creating and iterating moves efficiently and the art of finding the most promising subset of random moves to evaluate first.

1.2. What is a MoveSelector?

A MoveSelector's main function is to create Iterator<Move> when needed. An optimization algorithm will iterate through a subset of those moves.

Here’s an example how to configure a changeMoveSelector for the optimization algorithm Local Search:

  <localSearch>
    <changeMoveSelector/>
    ...
  </localSearch>

Out of the box, this works and all properties of the changeMoveSelector are defaulted sensibly (unless that fails fast due to ambiguity). On the other hand, the configuration can be customized significantly for specific use cases. For example: you might want to configure a filter to discard pointless moves.

1.3. Subselecting of entities, values, and other moves

To create a Move, a MoveSelector needs to select one or more planning entities and/or planning values to move. Just like MoveSelectors, EntitySelectors and ValueSelectors need to support a similar feature set (such as scalable just-in-time selection). Therefore, they all implement a common interface Selector and they are configured similarly.

A MoveSelector is often composed out of EntitySelectors, ValueSelectors or even other MoveSelectors, which can be configured individually if desired:

    <unionMoveSelector>
      <changeMoveSelector>
        <entitySelector>
          ...
        </entitySelector>
        <valueSelector>
          ...
        </valueSelector>
        ...
      </changeMoveSelector>
      <swapMoveSelector>
        ...
      </swapMoveSelector>
    </unionMoveSelector>

Together, this structure forms a Selector tree:

selectorTree

The root of this tree is a MoveSelector which is injected into the optimization algorithm implementation to be (partially) iterated in every step.

2. Generic MoveSelectors

2.1. Generic MoveSelectors overview

Name Description toString() example

Change move

Change 1 entity’s variable

Process-A {Computer-1 -> Computer-2}

Swap move

Swap all variables of 2 entities

Process-A {Computer-1} <-> Process-B {Computer-2}

Pillar change move

Change a set of entities with the same value

[Process-A, Process-B, Process-C] {Computer-1 -> Computer-2}

Pillar swap move

Swap 2 sets of entities with the same values

[Process-A, Process-B, Process-C] {Computer-1} <-> [Process-E, Process-F] {Computer-2}

Tail chain swap move

Swap 2 tails chains

Visit-A5 {Visit-A4} <-tailChainSwap-> Visit-B3 {Visit-B2}

Sub chain change move

Cut a subchain and paste it into another chain

[Visit-A5..Visit-A8] {Visit-A4 -> Visit-B2}

Sub chain swap move

Swap 2 subchains

[Visit-A5..Visit-A8] {Visit-A4} <-> [Visit-B3..Visit-B9] {Visit-B2}

2.2. ChangeMoveSelector

For one planning variable, the ChangeMove selects one planning entity and one planning value and assigns the entity’s variable to that value.

changeMove

Simplest configuration:

    <changeMoveSelector/>

If there are multiple entity classes or multiple planning variables for one entity class, a simple configuration will automatically unfold into a union of ChangeMove selectors for every planning variable.

Advanced configuration:

    <changeMoveSelector>
      ... <!-- Normal selector properties -->
      <entitySelector>
        <entityClass>...Lecture</entityClass>
        ...
      </entitySelector>
      <valueSelector variableName="room">
        ...
        <nearbySelection>...</nearbySelection>
      </valueSelector>
    </changeMoveSelector>

A ChangeMove is the finest grained move.

Almost every moveSelector configuration injected into a metaheuristic algorithm should include a changeMoveSelector. This guarantees that every possible solution can be reached in theory through applying a number of moves in sequence. Of course, normally it is unioned with other, more coarse grained move selectors.

This move selector only supports phase or solver caching if it doesn’t apply on a chained variable.

2.3. SwapMoveSelector

The SwapMove selects two different planning entities and swaps the planning values of all their planning variables.

swapMove

Although a SwapMove on a single variable is essentially just two ChangeMoves, it’s often the winning step in cases that the first of the two ChangeMoves would not win because it leaves the solution in a state with broken hard constraints. For example: swapping the room of two lectures doesn’t bring the solution in an intermediate state where both lectures are in the same room which breaks a hard constraint.

Simplest configuration:

    <swapMoveSelector/>

If there are multiple entity classes, a simple configuration will automatically unfold into a union of SwapMove selectors for every entity class.

Advanced configuration:

    <swapMoveSelector>
      ... <!-- Normal selector properties -->
      <entitySelector>
        <entityClass>...Lecture</entityClass>
        ...
      </entitySelector>
      <secondaryEntitySelector>
        <entityClass>...Lecture</entityClass>
        ...
        <nearbySelection>...</nearbySelection>
      </secondaryEntitySelector>
      <variableNameIncludes>
        <variableNameInclude>room</variableNameInclude>
        <variableNameInclude>...</variableNameInclude>
      </variableNameIncludes>
    </swapMoveSelector>

The secondaryEntitySelector is rarely needed: if it is not specified, entities from the same entitySelector are swapped.

If one or more variableNameInclude properties are specified, not all planning variables will be swapped, but only those specified. For example for course scheduling, specifying only variableNameInclude room will make it only swap room, not period.

This move selector only supports phase or solver caching if it doesn’t apply on any chained variables.

2.4. Pillar-based move selectors

A pillar is a set of planning entities which have the same planning value(s) for their planning variable(s).

2.4.1. PillarChangeMoveSelector

The PillarChangeMove selects one entity pillar (or subset of those) and changes the value of one variable (which is the same for all entities) to another value.

pillarChangeMove

In the example above, queen A and C have the same value (row 0) and are moved to row 2. Also the yellow and blue process have the same value (computer Y) and are moved to computer X.

Simplest configuration:

    <pillarChangeMoveSelector/>

Advanced configuration:

    <pillarChangeMoveSelector>
      <subPillarType>SEQUENCE</subPillarType>
      <subPillarSequenceComparatorClass>org.optaplanner.examples.nurserostering.domain.ShiftAssignmentComparator</subPillarSequenceComparatorClass>
      ... <!-- Normal selector properties -->
      <pillarSelector>
        <entitySelector>
          <entityClass>...ShiftAssignment</entityClass>
          ...
        </entitySelector>
        <minimumSubPillarSize>1</minimumSubPillarSize>
        <maximumSubPillarSize>1000</maximumSubPillarSize>
      </pillarSelector>
      <valueSelector variableName="room">
        ...
      </valueSelector>
    </pillarChangeMoveSelector>

For a description of subPillarType and related properties, please refer to Subpillars.

The other properties are explained in changeMoveSelector. This move selector does not support phase or solver caching and step caching scales badly memory wise.

2.4.2. PillarSwapMoveSelector

The PillarSwapMove selects two different entity pillars and swaps the values of all their variables for all their entities.

pillarSwapMove

Simplest configuration:

    <pillarSwapMoveSelector/>

Advanced configuration:

    <pillarSwapMoveSelector>
      <subPillarType>SEQUENCE</subPillarType>
      <subPillarSequenceComparatorClass>org.optaplanner.examples.nurserostering.domain.ShiftAssignmentComparator</subPillarSequenceComparatorClass>
      ... <!-- Normal selector properties -->
      <pillarSelector>
        <entitySelector>
          <entityClass>...ShiftAssignment</entityClass>
          ...
        </entitySelector>
        <minimumSubPillarSize>1</minimumSubPillarSize>
        <maximumSubPillarSize>1000</maximumSubPillarSize>
      </pillarSelector>
      <secondaryPillarSelector>
        <entitySelector>
          ...
        </entitySelector>
        ...
      </secondaryPillarSelector>
      <variableNameIncludes>
        <variableNameInclude>employee</variableNameInclude>
        <variableNameInclude>...</variableNameInclude>
      </variableNameIncludes>
    </pillarSwapMoveSelector>

For a description of subPillarType and related properties, please refer to sub pillars.

The secondaryPillarSelector is rarely needed: if it is not specified, entities from the same pillarSelector are swapped.

The other properties are explained in swapMoveSelector and pillarChangeMoveSelector. This move selector does not support phase or solver caching and step caching scales badly memory wise.

2.4.3. Sub pillars

A sub pillar is a subset of entities that share the same value(s) for their variable(s). For example if queen A, B, C and D are all located on row 0, they are a pillar and [A, D] is one of the many sub pillars.

There are several ways how sub pillars can be selected by the subPillarType property:

  • ALL (default) selects all possible sub pillars.

  • SEQUENCE limits selection of sub pillars to Sequential sub pillars.

  • NONE never selects any sub pillars.

If sub pillars are enabled, the pillar itself is also included and the properties minimumSubPillarSize (defaults to 1) and maximumSubPillarSize (defaults to infinity) limit the size of the selected (sub) pillar.

The number of sub pillars of a pillar is exponential to the size of the pillar. For example a pillar of size 32 has (2^32 - 1) subpillars. Therefore a pillarSelector only supports JIT random selection (which is the default).

Sequential sub pillars

Sub pillars can be sorted with a Comparator. A sequential sub pillar is a continuous subset of its sorted base pillar.

For example if a nurse has shifts on Monday (M), Tuesday (T), and Wednesday (W), they are a pillar and only the following are its sequential sub pillars: [M], [T], [W], [M, T], [T, W], [M, T, W]. But [M, W] is not a sub pillar in this case, as there is a gap on Tuesday.

Sequential sub pillars apply to both Pillar change move and Pillar swap move. A minimal configuration looks like this:

    <pillar...MoveSelector>
      <subPillarType>SEQUENCE</subPillarType>
    </pillar...MoveSelector>

In this case, the entity being operated on must implement the Comparable interface. The size of sub pillars will not be limited in any way.

An advanced configuration looks like this:

    <pillar...MoveSelector>
      ...
      <subPillarType>SEQUENCE</subPillarType>
      <subPillarSequenceComparatorClass>org.optaplanner.examples.nurserostering.domain.ShiftAssignmentComparator</subPillarSequenceComparatorClass>
      <pillarSelector>
        ...
        <minimumSubPillarSize>1</minimumSubPillarSize>
        <maximumSubPillarSize>1000</maximumSubPillarSize>
      </pillarSelector>
      ...
    </pillar...MoveSelector>

In this case, the entity being operated on need not be Comparable. The given subPillarSequenceComparatorClass is used to establish the sequence instead. Also, the size of the sub pillars is limited in length of up to 1000 entities.

2.5. Move selectors for chained variables

2.5.1. TailChainSwapMoveSelector or 2-opt

A tailChain is a set of planning entities with a chained planning variable which form the last part of a chain. The tailChainSwapMove selects a tail chain and swaps it with the tail chain of another planning value (in a different or the same anchor chain). If the targeted planning value, doesn’t have a tail chain, it swaps with nothing (resulting in a change like move). If it occurs within the same anchor chain, a partial chain reverse occurs. In academic papers, this is often called a 2-opt move.

Simplest configuration:

    <tailChainSwapMoveSelector/>

Advanced configuration:

    <tailChainSwapMoveSelector>
      ... <!-- Normal selector properties -->
      <entitySelector>
        <entityClass>...Customer</entityClass>
        ...
      </entitySelector>
      <valueSelector variableName="previousStandstill">
        ...
        <nearbySelection>...</nearbySelection>
      </valueSelector>
    </tailChainSwapMoveSelector>

The entitySelector selects the start of the tail chain that is being moved. The valueSelector selects to where that tail chain is moved. If it has a tail chain itself, that is moved to the location of the original tail chain. It uses a valueSelector instead of a secondaryEntitySelector to be able to include all possible 2opt moves (such as moving to the end of a tail) and to work correctly with nearby selection (because of asymmetric distances and also swapped entity distance gives an incorrect selection probability).

Although subChainChangeMoveSelector and subChainSwapMoveSelector include almost every possible tailChainSwapMove, experiments have shown that focusing on tailChainSwapMoves increases efficiency.

This move selector does not support phase or solver caching.

2.5.2. SubChainChangeMoveSelector

A subChain is a set of planning entities with a chained planning variable which form part of a chain. The subChainChangeMoveSelector selects a subChain and moves it to another place (in a different or the same anchor chain).

Simplest configuration:

    <subChainChangeMoveSelector/>

Advanced configuration:

    <subChainChangeMoveSelector>
      ... <!-- Normal selector properties -->
      <entityClass>...Customer</entityClass>
      <subChainSelector>
        <valueSelector variableName="previousStandstill">
          ...
        </valueSelector>
        <minimumSubChainSize>2</minimumSubChainSize>
        <maximumSubChainSize>40</maximumSubChainSize>
      </subChainSelector>
      <valueSelector variableName="previousStandstill">
        ...
      </valueSelector>
      <selectReversingMoveToo>true</selectReversingMoveToo>
    </subChainChangeMoveSelector>

The subChainSelector selects a number of entities, no less than minimumSubChainSize (defaults to 1) and no more than maximumSubChainSize (defaults to infinity).

If minimumSubChainSize is 1 (which is the default), this selector might select the same move as a ChangeMoveSelector, at a far lower selection probability (because each move type has the same selection chance by default (not every move instance) and there are far more SubChainChangeMove instances than ChangeMove instances). However, don’t just remove the ChangeMoveSelector, because experiments show that it’s good to focus on ChangeMoves.

Furthermore, in a SubChainSwapMoveSelector, setting minimumSubChainSize prevents swapping a subchain of size 1 with a subchain of size 2 or more.

The selectReversingMoveToo property (defaults to true) enables selecting the reverse of every subchain too.

This move selector does not support phase or solver caching and step caching scales badly memory wise.

2.5.3. SubChainSwapMoveSelector

The subChainSwapMoveSelector selects two different subChains and moves them to another place in a different or the same anchor chain.

Simplest configuration:

    <subChainSwapMoveSelector/>

Advanced configuration:

    <subChainSwapMoveSelector>
      ... <!-- Normal selector properties -->
      <entityClass>...Customer</entityClass>
      <subChainSelector>
        <valueSelector variableName="previousStandstill">
          ...
        </valueSelector>
        <minimumSubChainSize>2</minimumSubChainSize>
        <maximumSubChainSize>40</maximumSubChainSize>
      </subChainSelector>
      <secondarySubChainSelector>
        <valueSelector variableName="previousStandstill">
          ...
        </valueSelector>
        <minimumSubChainSize>2</minimumSubChainSize>
        <maximumSubChainSize>40</maximumSubChainSize>
      </secondarySubChainSelector>
      <selectReversingMoveToo>true</selectReversingMoveToo>
    </subChainSwapMoveSelector>

The secondarySubChainSelector is rarely needed: if it is not specified, entities from the same subChainSelector are swapped.

The other properties are explained in subChainChangeMoveSelector. This move selector does not support phase or solver caching and step caching scales badly memory wise.

3. Combining multiple MoveSelectors

3.1. unionMoveSelector

A unionMoveSelector selects a Move by selecting one of its MoveSelector children to supply the next Move.

Simplest configuration:

    <unionMoveSelector>
      <...MoveSelector/>
      <...MoveSelector/>
      <...MoveSelector/>
      ...
    </unionMoveSelector>

Advanced configuration:

    <unionMoveSelector>
      ... <!-- Normal selector properties -->
      <changeMoveSelector>
        <fixedProbabilityWeight>...</fixedProbabilityWeight>
        ...
      </changeMoveSelector>
      <swapMoveSelector>
        <fixedProbabilityWeight>...</fixedProbabilityWeight>
        ...
      </swapMoveSelector>
      <...MoveSelector>
        <fixedProbabilityWeight>...</fixedProbabilityWeight>
        ...
      </...MoveSelector>
      ...
      <selectorProbabilityWeightFactoryClass>...ProbabilityWeightFactory</selectorProbabilityWeightFactoryClass>
    </unionMoveSelector>

The selectorProbabilityWeightFactory determines in selectionOrder RANDOM how often a MoveSelector child is selected to supply the next Move. By default, each MoveSelector child has the same chance of being selected.

selectorProbabilityInUnion

Change the fixedProbabilityWeight of such a child to select it more often. For example, the unionMoveSelector can return a SwapMove twice as often as a ChangeMove:

    <unionMoveSelector>
      <changeMoveSelector>
        <fixedProbabilityWeight>1.0</fixedProbabilityWeight>
        ...
      </changeMoveSelector>
      <swapMoveSelector>
        <fixedProbabilityWeight>2.0</fixedProbabilityWeight>
        ...
      </swapMoveSelector>
    </unionMoveSelector>

The number of possible ChangeMoves is very different from the number of possible SwapMoves and furthermore it’s problem dependent. To give each individual Move the same selection chance (as opposed to each MoveSelector), use the FairSelectorProbabilityWeightFactory:

    <unionMoveSelector>
      <changeMoveSelector/>
      <swapMoveSelector/>
      <selectorProbabilityWeightFactoryClass>org.optaplanner.core.impl.heuristic.selector.common.decorator.FairSelectorProbabilityWeightFactory</selectorProbabilityWeightFactoryClass>
    </unionMoveSelector>

3.2. cartesianProductMoveSelector

A cartesianProductMoveSelector selects a new CompositeMove. It builds that CompositeMove by selecting one Move per MoveSelector child and adding it to the CompositeMove.

Simplest configuration:

    <cartesianProductMoveSelector>
      <...MoveSelector/>
      <...MoveSelector/>
      <...MoveSelector/>
      ...
    </cartesianProductMoveSelector>

Advanced configuration:

    <cartesianProductMoveSelector>
      ... <!-- Normal selector properties -->
      <changeMoveSelector>
        ...
      </changeMoveSelector>
      <swapMoveSelector>
        ...
      </swapMoveSelector>
      <...MoveSelector>
        ...
      </...MoveSelector>
      ...
      <ignoreEmptyChildIterators>true</ignoreEmptyChildIterators>
    </cartesianProductMoveSelector>

The ignoreEmptyChildIterators property (true by default) will ignore every empty childMoveSelector to avoid returning no moves. For example: a cartesian product of changeMoveSelector A and B, for which B is empty (because all it’s entities are pinned) returns no move if ignoreEmptyChildIterators is false and the moves of A if ignoreEmptyChildIterators is true.

To enforce that two child selectors use the same entity or value efficiently, use mimic selection, not move filtering.

4. EntitySelector

Simplest configuration:

      <entitySelector/>

Advanced configuration:

      <entitySelector>
        ... <!-- Normal selector properties -->
        <entityClass>org.optaplanner.examples.curriculumcourse.domain.Lecture</entityClass>
      </entitySelector>

The entityClass property is only required if it cannot be deduced automatically because there are multiple entity classes.

5. ValueSelector

Simplest configuration:

      <valueSelector/>

Advanced configuration:

      <valueSelector variableName="room">
        ... <!-- Normal selector properties -->
      </valueSelector>

The variableName property is only required if it cannot be deduced automatically because there are multiple variables (for the related entity class).

In exotic Construction Heuristic configurations, the entityClass from the EntitySelector sometimes needs to be downcasted, which can be done with the property downcastEntityClass:

      <valueSelector variableName="period">
        <downcastEntityClass>...LeadingExam</downcastEntityClass>
      </valueSelector>

If a selected entity cannot be downcasted, the ValueSelector is empty for that entity.

6. General Selector features

6.1. CacheType: create moves ahead of time or just in time

A Selector's cacheType determines when a selection (such as a Move, an entity, a value, …​) is created and how long it lives.

Almost every Selector supports setting a cacheType:

    <changeMoveSelector>
      <cacheType>PHASE</cacheType>
      ...
    </changeMoveSelector>

The following cacheTypes are supported:

  • JUST_IN_TIME (default, recommended): Not cached. Construct each selection (Move, …​) just before it’s used. This scales up well in memory footprint.

  • STEP: Cached. Create each selection (Move, …​) at the beginning of a step and cache them in a list for the remainder of the step. This scales up badly in memory footprint.

  • PHASE: Cached. Create each selection (Move, …​) at the beginning of a solver phase and cache them in a list for the remainder of the phase. Some selections cannot be phase cached because the list changes every step. This scales up badly in memory footprint, but has a slight performance gain.

  • SOLVER: Cached. Create each selection (Move, …​) at the beginning of a Solver and cache them in a list for the remainder of the Solver. Some selections cannot be solver cached because the list changes every step. This scales up badly in memory footprint, but has a slight performance gain.

A cacheType can be set on composite selectors too:

    <unionMoveSelector>
      <cacheType>PHASE</cacheType>
      <changeMoveSelector/>
      <swapMoveSelector/>
      ...
    </unionMoveSelector>

Nested selectors of a cached selector cannot be configured to be cached themselves, unless it’s a higher cacheType. For example: a STEP cached unionMoveSelector can contain a PHASE cached changeMoveSelector, but it cannot contain a STEP cached changeMoveSelector.

6.2. SelectionOrder: original, sorted, random, shuffled, or probabilistic

A Selector's selectionOrder determines the order in which the selections (such as Moves, entities, values, …​) are iterated. An optimization algorithm will usually only iterate through a subset of its MoveSelector's selections, starting from the start, so the selectionOrder is critical to decide which Moves are actually evaluated.

Almost every Selector supports setting a selectionOrder:

    <changeMoveSelector>
      ...
      <selectionOrder>RANDOM</selectionOrder>
      ...
    </changeMoveSelector>

The following selectionOrders are supported:

  • ORIGINAL: Select the selections (Moves, entities, values, …​) in default order. Each selection will be selected only once.

    • For example: A0, A1, A2, A3, …​, B0, B1, B2, B3, …​, C0, C1, C2, C3, …​

  • SORTED: Select the selections (Moves, entities, values, …​) in sorted order. Each selection will be selected only once. Requires cacheType >= STEP. Mostly used on an entitySelector or valueSelector for construction heuristics. See sorted selection.

    • For example: A0, B0, C0, …​, A2, B2, C2, …​, A1, B1, C1, …​

  • RANDOM (default): Select the selections (Moves, entities, values, …​) in non-shuffled random order. A selection might be selected multiple times. This scales up well in performance because it does not require caching.

    • For example: C2, A3, B1, C2, A0, C0, …​

  • SHUFFLED: Select the selections (Moves, entities, values, …​) in shuffled random order. Each selection will be selected only once. Requires cacheType >= STEP. This scales up badly in performance, not just because it requires caching, but also because a random number is generated for each element, even if it’s not selected (which is the grand majority when scaling up).

    • For example: C2, A3, B1, A0, C0, …​

  • PROBABILISTIC: Select the selections (Moves, entities, values, …​) in random order, based on the selection probability of each element. A selection with a higher probability has a higher chance to be selected than elements with a lower probability. A selection might be selected multiple times. Requires cacheType >= STEP. Mostly used on an entitySelector or valueSelector. See probabilistic selection.

    • For example: B1, B1, A1, B2, B1, C2, B1, B1, …​

A selectionOrder can be set on composite selectors too.

When a Selector is cached, all of its nested Selectors will naturally default to selectionOrder ORIGINAL. Avoid overwriting the selectionOrder of those nested Selectors.

6.3. Recommended combinations of CacheType and SelectionOrder

6.3.1. Just in time random selection (default)

This combination is great for big use cases (10 000 entities or more), as it scales up well in memory footprint and performance. Other combinations are often not even viable on such sizes. It works for smaller use cases too, so it’s a good way to start out. It’s the default, so this explicit configuration of cacheType and selectionOrder is actually obsolete:

    <unionMoveSelector>
      <cacheType>JUST_IN_TIME</cacheType>
      <selectionOrder>RANDOM</selectionOrder>

      <changeMoveSelector/>
      <swapMoveSelector/>
    </unionMoveSelector>

Here’s how it works. When Iterator<Move>.next() is called, a child MoveSelector is randomly selected (1), which creates a random Move (2, 3, 4) and is then returned (5):

jitRandomSelection

Notice that it never creates a list of Moves and it generates random numbers only for Moves that are actually selected.

6.3.2. Cached shuffled selection

This combination often wins for small use cases (1000 entities or less). Beyond that size, it scales up badly in memory footprint and performance.

    <unionMoveSelector>
      <cacheType>PHASE</cacheType>
      <selectionOrder>SHUFFLED</selectionOrder>

      <changeMoveSelector/>
      <swapMoveSelector/>
    </unionMoveSelector>

Here’s how it works: At the start of the phase (or step depending on the cacheType), all moves are created (1) and cached (2). When MoveSelector.iterator() is called, the moves are shuffled (3). When Iterator<Move>.next() is called, the next element in the shuffled list is returned (4):

cachedShuffledSelection

Notice that each Move will only be selected once, even though they are selected in random order.

Use cacheType PHASE if none of the (possibly nested) Selectors require STEP. Otherwise, do something like this:

    <unionMoveSelector>
      <cacheType>STEP</cacheType>
      <selectionOrder>SHUFFLED</selectionOrder>

      <changeMoveSelector>
        <cacheType>PHASE</cacheType>
      </changeMoveSelector>
      <swapMoveSelector>
        <cacheType>PHASE</cacheType>
      </swapMoveSelector>
      <pillarSwapMoveSelector/><!-- Does not support cacheType PHASE -->
    </unionMoveSelector>

6.3.3. Cached random selection

This combination is often a worthy competitor for medium use cases, especially with fast stepping optimization algorithms (such as Simulated Annealing). Unlike cached shuffled selection, it doesn’t waste time shuffling the moves list at the beginning of every step.

    <unionMoveSelector>
      <cacheType>PHASE</cacheType>
      <selectionOrder>RANDOM</selectionOrder>

      <changeMoveSelector/>
      <swapMoveSelector/>
    </unionMoveSelector>

6.4. Filtered selection

There can be certain moves that you don’t want to select, because:

  • The move is pointless and would only waste CPU time. For example, swapping two lectures of the same course will result in the same score and the same schedule because all lectures of one course are interchangeable (same teacher, same students, same topic).

  • Doing the move would break a built-in hard constraint, so the solution would be infeasible but the score function doesn’t check built-in hard constraints for performance reasons. For example, don’t change a gym lecture to a room which is not a gym room. It’s usually better to not use move filtering for such cases, because it allows the metaheuristics to temporarily break hard constraints to escape local optima.

    Any built-in hard constraint must probably be filtered on every move type of every solver phase. For example if it filters the change move of Local Search, it must also filter the swap move that swaps the room of a gym lecture with another lecture for which the other lecture’s original room isn’t a gym room. Furthermore, it must also filter the change moves of the Construction Heuristics (which requires an advanced configuration).

If a move is unaccepted by the filter, it’s not executed and the score isn’t calculated.

filteredSelection

Filtering uses the interface SelectionFilter:

public interface SelectionFilter<Solution_, T> {

    boolean accept(ScoreDirector<Solution_> scoreDirector, T selection);

}

Implement the accept method to return false on a discarded selection (see below). Filtered selection can happen on any Selector in the selector tree, including any MoveSelector, EntitySelector or ValueSelector. It works with any cacheType and selectionOrder.

Apply the filter on the lowest level possible. In most cases, you’ll need to know both the entity and the value involved so you’ll have to apply it on the move selector.

6.4.1. Filtered move selection

Unaccepted moves will not be selected and will therefore never have their doMove() method called:

public class DifferentCourseSwapMoveFilter implements SelectionFilter<CourseSchedule, SwapMove> {

    @Override
    public boolean accept(ScoreDirector<CourseSchedule> scoreDirector, SwapMove move) {
        Lecture leftLecture = (Lecture) move.getLeftEntity();
        Lecture rightLecture = (Lecture) move.getRightEntity();
        return !leftLecture.getCourse().equals(rightLecture.getCourse());
    }

}

Configure the filterClass on every targeted moveSelector (potentially both in the Local Search and the Construction Heuristics if it filters ChangeMoves):

    <swapMoveSelector>
      <filterClass>org.optaplanner.examples.curriculumcourse.solver.move.DifferentCourseSwapMoveFilter</filterClass>
    </swapMoveSelector>

6.4.2. Filtered entity selection

Unaccepted entities will not be selected and will therefore never be used to create a move.

public class LongLectureSelectionFilter implements SelectionFilter<CourseSchedule, Lecture> {

    @Override
    public boolean accept(ScoreDirector<CourseSchedule> scoreDirector, Lecture lecture) {
        return lecture.isLong();
    }

}

Configure the filterClass on every targeted entitySelector (potentially both in the Local Search and the Construction Heuristics):

    <changeMoveSelector>
      <entitySelector>
        <filterClass>org.optaplanner.examples.curriculumcourse.solver.move.LongLectureSelectionFilter</filterClass>
      </entitySelector>
    </changeMoveSelector>

If that filter should apply on all entities, configure it as a global pinningFilter instead.

6.4.3. Filtered value selection

Unaccepted values will not be selected and will therefore never be used to create a move.

public class LongPeriodSelectionFilter implements SelectionFilter<CourseSchedule, Period> {

    @Override
    public boolean accept(ScoreDirector<CourseSchedule> scoreDirector, Period period) {
        return period();
    }

}

Configure the filterClass on every targeted valueSelector (potentially both in the Local Search and the Construction Heuristics):

    <changeMoveSelector>
      <valueSelector>
        <filterClass>org.optaplanner.examples.curriculumcourse.solver.move.LongPeriodSelectionFilter</filterClass>
      </valueSelector>
    </changeMoveSelector>

6.5. Sorted selection

Sorted selection can happen on any Selector in the selector tree, including any MoveSelector, EntitySelector or ValueSelector. It does not work with cacheType JUST_IN_TIME and it only works with selectionOrder SORTED.

It’s mostly used in construction heuristics.

If the chosen construction heuristic implies sorting, for example FIRST_FIT_DECREASING implies that the EntitySelector is sorted, there is no need to explicitly configure a Selector with sorting. If you do explicitly configure the Selector, it overwrites the default settings of that construction heuristic.

6.5.1. Sorted selection by SorterManner

Some Selector types implement a SorterManner out of the box:

  • EntitySelector supports:

    • DECREASING_DIFFICULTY: Sorts the planning entities according to decreasing planning entity difficulty. Requires that planning entity difficulty is annotated on the domain model.

          <entitySelector>
            <cacheType>PHASE</cacheType>
            <selectionOrder>SORTED</selectionOrder>
            <sorterManner>DECREASING_DIFFICULTY</sorterManner>
          </entitySelector>
  • ValueSelector supports:

    • INCREASING_STRENGTH: Sorts the planning values according to increasing planning value strength. Requires that planning value strength is annotated on the domain model.

          <valueSelector>
            <cacheType>PHASE</cacheType>
            <selectionOrder>SORTED</selectionOrder>
            <sorterManner>INCREASING_STRENGTH</sorterManner>
          </valueSelector>

6.5.2. Sorted selection by Comparator

An easy way to sort a Selector is with a plain old Comparator:

public class CloudProcessDifficultyComparator implements Comparator<CloudProcess> {

    public int compare(CloudProcess a, CloudProcess b) {
        return new CompareToBuilder()
                .append(a.getRequiredMultiplicand(), b.getRequiredMultiplicand())
                .append(a.getId(), b.getId())
                .toComparison();
    }

}

You’ll also need to configure it (unless it’s annotated on the domain model and automatically applied by the optimization algorithm):

    <entitySelector>
      <cacheType>PHASE</cacheType>
      <selectionOrder>SORTED</selectionOrder>
      <sorterComparatorClass>...CloudProcessDifficultyComparator</sorterComparatorClass>
      <sorterOrder>DESCENDING</sorterOrder>
    </entitySelector>

6.5.3. Sorted selection by SelectionSorterWeightFactory

If you need the entire solution to sort a Selector, use a SelectionSorterWeightFactory instead:

public interface SelectionSorterWeightFactory<Solution_, T> {

    Comparable createSorterWeight(Solution_ solution, T selection);

}
public class QueenDifficultyWeightFactory implements SelectionSorterWeightFactory<NQueens, Queen> {

    public QueenDifficultyWeight createSorterWeight(NQueens nQueens, Queen queen) {
        int distanceFromMiddle = calculateDistanceFromMiddle(nQueens.getN(), queen.getColumnIndex());
        return new QueenDifficultyWeight(queen, distanceFromMiddle);
    }

    ...

    public static class QueenDifficultyWeight implements Comparable<QueenDifficultyWeight> {

        private final Queen queen;
        private final int distanceFromMiddle;

        public QueenDifficultyWeight(Queen queen, int distanceFromMiddle) {
            this.queen = queen;
            this.distanceFromMiddle = distanceFromMiddle;
        }

        public int compareTo(QueenDifficultyWeight other) {
            return new CompareToBuilder()
                    // The more difficult queens have a lower distance to the middle
                    .append(other.distanceFromMiddle, distanceFromMiddle) // Decreasing
                    // Tie breaker
                    .append(queen.getColumnIndex(), other.queen.getColumnIndex())
                    .toComparison();
        }

    }

}

You’ll also need to configure it (unless it’s annotated on the domain model and automatically applied by the optimization algorithm):

    <entitySelector>
      <cacheType>PHASE</cacheType>
      <selectionOrder>SORTED</selectionOrder>
      <sorterWeightFactoryClass>...QueenDifficultyWeightFactory</sorterWeightFactoryClass>
      <sorterOrder>DESCENDING</sorterOrder>
    </entitySelector>

6.5.4. Sorted selection by SelectionSorter

Alternatively, you can also use the interface SelectionSorter directly:

public interface SelectionSorter<Solution_, T> {

    void sort(ScoreDirector<Solution_> scoreDirector, List<T> selectionList);

}
    <entitySelector>
      <cacheType>PHASE</cacheType>
      <selectionOrder>SORTED</selectionOrder>
      <sorterClass>...MyEntitySorter</sorterClass>
    </entitySelector>

6.6. Probabilistic selection

Probabilistic selection can happen on any Selector in the selector tree, including any MoveSelector, EntitySelector or ValueSelector. It does not work with cacheType JUST_IN_TIME and it only works with selectionOrder PROBABILISTIC.

probabilisticSelection

Each selection has a probabilityWeight, which determines the chance that selection will be selected:

public interface SelectionProbabilityWeightFactory<Solution_, T> {

    double createProbabilityWeight(ScoreDirector<Solution_> scoreDirector, T selection);

}
    <entitySelector>
      <cacheType>PHASE</cacheType>
      <selectionOrder>PROBABILISTIC</selectionOrder>
      <probabilityWeightFactoryClass>...MyEntityProbabilityWeightFactoryClass</probabilityWeightFactoryClass>
    </entitySelector>

For example, if there are three entities: process A (probabilityWeight 2.0), process B (probabilityWeight 0.5) and process C (probabilityWeight 0.5), then process A will be selected four times more than B and C.

6.7. Limited selection

Selecting all possible moves sometimes does not scale well enough, especially for construction heuristics (which don’t support acceptedCountLimit).

To limit the number of selected selection per step, apply a selectedCountLimit on the selector:

    <changeMoveSelector>
      <selectedCountLimit>100</selectedCountLimit>
    </changeMoveSelector>

To scale Local Search, setting acceptedCountLimit is usually better than using selectedCountLimit.

6.8. Mimic selection (record/replay)

During mimic selection, one normal selector records its selection and one or multiple other special selectors replay that selection. The recording selector acts as a normal selector and supports all other configuration properties. A replaying selector mimics the recording selection and supports no other configuration properties.

The recording selector needs an id. A replaying selector must reference a recorder’s id with a mimicSelectorRef:

      <cartesianProductMoveSelector>
        <changeMoveSelector>
          <entitySelector id="entitySelector"/>
          <valueSelector variableName="period"/>
        </changeMoveSelector>
        <changeMoveSelector>
          <entitySelector mimicSelectorRef="entitySelector"/>
          <valueSelector variableName="room"/>
        </changeMoveSelector>
      </cartesianProductMoveSelector>

Mimic selection is useful to create a composite move from two moves that affect the same entity.

6.9. Nearby selection

In some use cases (such as TSP and VRP, but also in non-chained variable cases), changing entities to nearby values or swapping nearby entities can heavily increase scalability and improve solution quality.

nearbySelectionMotivation

Nearby selection increases the probability of selecting an entity or value which is nearby to the first entity being moved in that move.

nearbySelectionRandomDistribution

The distance between two entities or values is domain specific. Therefore, implement the NearbyDistanceMeter interface:

public interface NearbyDistanceMeter<O, D> {

    double getNearbyDistance(O origin, D destination);

}

It returns a double which represents the distance:

public class CustomerNearbyDistanceMeter implements NearbyDistanceMeter<Customer, Standstill> {

    public double getNearbyDistance(Customer origin, Standstill destination) {
        return origin.getDistanceTo(destination);
    }

}

To configure nearby selection, add a nearbySelection element in the entitySelector or valueSelector and use mimic selection to specify which entity should be near by the selection.

    <unionMoveSelector>
      <changeMoveSelector>
        <entitySelector id="entitySelector1"/>
        <valueSelector>
          <nearbySelection>
            <originEntitySelector mimicSelectorRef="entitySelector1"/>
            <nearbyDistanceMeterClass>...CustomerNearbyDistanceMeter</nearbyDistanceMeterClass>
            <parabolicDistributionSizeMaximum>40</parabolicDistributionSizeMaximum>
          </nearbySelection>
        </valueSelector>
      </changeMoveSelector>
      <swapMoveSelector>
        <entitySelector id="entitySelector2"/>
        <secondaryEntitySelector>
          <nearbySelection>
            <originEntitySelector mimicSelectorRef="entitySelector2"/>
            <nearbyDistanceMeterClass>...CustomerNearbyDistanceMeter</nearbyDistanceMeterClass>
            <parabolicDistributionSizeMaximum>40</parabolicDistributionSizeMaximum>
          </nearbySelection>
        </secondaryEntitySelector>
      </swapMoveSelector>
      <tailChainSwapMoveSelector>
        <entitySelector id="entitySelector3"/>
        <valueSelector>
          <nearbySelection>
            <originEntitySelector mimicSelectorRef="entitySelector3"/>
            <nearbyDistanceMeterClass>...CustomerNearbyDistanceMeter</nearbyDistanceMeterClass>
            <parabolicDistributionSizeMaximum>40</parabolicDistributionSizeMaximum>
          </nearbySelection>
        </valueSelector>
      </tailChainSwapMoveSelector>
    </unionMoveSelector>

A distributionSizeMaximum parameter should not be 1 because if the nearest is already the planning value of the current entity, then the only move that is selectable is not doable.

To allow every element to be selected, regardless of the number of entities, only set the distribution type (so without a distributionSizeMaximum parameter):

  <nearbySelection>
    <nearbySelectionDistributionType>PARABOLIC_DISTRIBUTION</nearbySelectionDistributionType>
  </nearbySelection>

The following NearbySelectionDistributionTypes are supported:

  • BLOCK_DISTRIBUTION: Only the n nearest are selected, with an equal probability. For example, select the 20 nearest:

      <nearbySelection>
        <blockDistributionSizeMaximum>20</blockDistributionSizeMaximum>
      </nearbySelection>
  • LINEAR_DISTRIBUTION: Nearest elements are selected with a higher probability. The probability decreases linearly.

      <nearbySelection>
        <linearDistributionSizeMaximum>40</linearDistributionSizeMaximum>
      </nearbySelection>
  • PARABOLIC_DISTRIBUTION (recommended): Nearest elements are selected with a higher probability.

      <nearbySelection>
        <parabolicDistributionSizeMaximum>80</parabolicDistributionSizeMaximum>
      </nearbySelection>
  • BETA_DISTRIBUTION: Selection according to a beta distribution. Slows down the solver significantly.

      <nearbySelection>
        <betaDistributionAlpha>1</betaDistributionAlpha>
        <betaDistributionBeta>5</betaDistributionBeta>
      </nearbySelection>

As always, use the Benchmarker to tweak values if desired.

7. Custom moves

7.1. Which move types might be missing in my implementation?

To determine which move types might be missing in your implementation, run a Benchmarker for a short amount of time and configure it to write the best solutions to disk. Take a look at such a best solution: it will likely be a local optima. Try to figure out if there’s a move that could get out of that local optima faster.

If you find one, implement that coarse-grained move, mix it with the existing moves and benchmark it against the previous configurations to see if you want to keep it.

7.2. Custom moves introduction

Instead of using the generic Moves (such as ChangeMove) you can also implement your own Move. Generic and custom MoveSelectors can be combined as desired.

A custom Move can be tailored to work to the advantage of your constraints. For example in examination scheduling, changing the period of an exam A would also change the period of all the other exams that need to coincide with exam A.

A custom Move is far more work to implement and much harder to avoid bugs than a generic Move. After implementing a custom Move, turn on environmentMode FULL_ASSERT to check for score corruptions.

7.3. The Move interface

All moves implement the Move interface:

public interface Move<Solution_> {

    boolean isMoveDoable(ScoreDirector<Solution_> scoreDirector);

    Move<Solution_> doMove(ScoreDirector<Solution_> scoreDirector);

    ...
}

To implement a custom move, it’s recommended to extend AbstractMove instead implementing Move directly. OptaPlanner calls AbstractMove.doMove(ScoreDirector), which calls doMoveOnGenuineVariables(ScoreDirector). For example in cloud balancing, this move changes one process to another computer:

public class CloudComputerChangeMove extends AbstractMove<CloudBalance> {

    private CloudProcess cloudProcess;
    private CloudComputer toCloudComputer;

    public CloudComputerChangeMove(CloudProcess cloudProcess, CloudComputer toCloudComputer) {
        this.cloudProcess = cloudProcess;
        this.toCloudComputer = toCloudComputer;
    }

    @Override
    protected void doMoveOnGenuineVariables(ScoreDirector<CloudBalance> scoreDirector) {
        scoreDirector.beforeVariableChanged(cloudProcess, "computer");
        cloudProcess.setComputer(toCloudComputer);
        scoreDirector.afterVariableChanged(cloudProcess, "computer");
    }

    // ...

}

The implementation must notify the ScoreDirector of any changes it makes to planning entity’s variables: Call the scoreDirector.beforeVariableChanged(Object, String) and scoreDirector.afterVariableChanged(Object, String) methods directly before and after modifying an entity’s planning variable.

The example move above is a fine-grained move because it changes only one planning variable. On the other hand, a coarse-grained move changes multiple entities or multiple planning variables in a single move, usually to avoid breaking hard constraints by making multiple related changes at once. For example, a swap move is really just two change moves, but it keeps those two changes together.

A Move can only change/add/remove planning entities, it must not change any of the problem facts as that will cause score corruption. Use real-time planning to change problem facts while solving.

OptaPlanner automatically filters out non doable moves by calling the isMoveDoable(ScoreDirector) method on each selected move. A non doable move is:

  • A move that changes nothing on the current solution. For example, moving process P1 on computer X to computer X is not doable, because it is already there.

  • A move that is impossible to do on the current solution. For example, moving process P1 to computer Q (when Q isn’t in the list of computers) is not doable because it would assign a planning value that’s not inside the planning variable’s value range.

In the cloud balancing example, a move which assigns a process to the computer it’s already assigned to is not doable:

    @Override
    public boolean isMoveDoable(ScoreDirector<CloudBalance> scoreDirector) {
        return !Objects.equals(cloudProcess.getComputer(), toCloudComputer);
    }

We don’t need to check if toCloudComputer is in the value range, because we only generate moves for which that is the case. A move that is currently not doable can become doable when the working solution changes in a later step, otherwise we probably shouldn’t have created it in the first place.

Each move has an undo move: a move (normally of the same type) which does the exact opposite. In the cloud balancing example the undo move of P1 {X → Y} is the move P1 {Y → X}. The undo move of a move is created when the Move is being done on the current solution, before the genuine variables change:

    @Override
    public CloudComputerChangeMove createUndoMove(ScoreDirector<CloudBalance> scoreDirector) {
        return new CloudComputerChangeMove(cloudProcess, cloudProcess.getComputer());
    }

Notice that if P1 would have already been moved to Y, the undo move would create the move P1 {Y → Y}, instead of the move P1 {Y → X}.

A solver phase might do and undo the same Move more than once. In fact, many solver phases will iteratively do and undo a number of moves to evaluate them, before selecting one of those and doing that move again (without undoing it the last time).

Always implement the toString() method to keep OptaPlanner’s logs readable. Keep it non-verbose and make it consistent with the generic moves:

    public String toString() {
        return cloudProcess + " {" + cloudProcess.getComputer() + " -> " + toCloudComputer + "}";
    }

Optionally, implement the getSimpleMoveTypeDescription() method to support picked move statistics:

    @Override
    public String getSimpleMoveTypeDescription() {
        return "CloudComputerChangeMove(CloudProcess.computer)";
    }

7.3.1. Custom move: rebase()

For multithreaded incremental solving, the custom move must implement the rebase() method:

    @Override
    public CloudComputerChangeMove rebase(ScoreDirector<CloudBalance> destinationScoreDirector) {
        return new CloudComputerChangeMove(destinationScoreDirector.lookUpWorkingObject(cloudProcess),
                destinationScoreDirector.lookUpWorkingObject(toCloudComputer));
    }

Rebasing a move takes a move generated of one working solution and creates a new move that does the same change as the original move, but rewired as if was generated off of the destination working solution. This allows multithreaded solving to migrate moves from one thread to another.

The lookUpWorkingObject() method translates a planning entity instance or problem fact instance from one working solution to that of the destination’s working solution. Internally it often uses a mapping technique based on the planning ID.

To rebase lists or arrays in bulk, use rebaseList() and rebaseArray() on AbstractMove.

7.3.2. Custom move: getPlanningEntities() and getPlanningValues()

A custom move should also implement the getPlanningEntities() and getPlanningValues() methods. Those are used by entity tabu and value tabu respectively. They are called after the Move has already been done.

    @Override
    public Collection<? extends Object> getPlanningEntities() {
        return Collections.singletonList(cloudProcess);
    }

    @Override
    public Collection<? extends Object> getPlanningValues() {
        return Collections.singletonList(toCloudComputer);
    }

If the Move changes multiple planning entities, such as in a swap move, return all of them in getPlanningEntities() and return all their values (to which they are changing) in getPlanningValues().

    @Override
    public Collection<? extends Object> getPlanningEntities() {
        return Arrays.asList(leftCloudProcess, rightCloudProcess);
    }

    @Override
    public Collection<? extends Object> getPlanningValues() {
        return Arrays.asList(leftCloudProcess.getComputer(), rightCloudProcess.getComputer());
    }

7.3.3. Custom move: equals() and hashCode()

A Move must implement the equals() and hashCode() methods for move tabu. Two moves which make the same change on a solution, should be equal ideally.

    @Override
    public boolean equals(Object o) {
        if (this == o) {
            return true;
        } else if (o instanceof CloudComputerChangeMove) {
            CloudComputerChangeMove other = (CloudComputerChangeMove) o;
            return new EqualsBuilder()
                    .append(cloudProcess, other.cloudProcess)
                    .append(toCloudComputer, other.toCloudComputer)
                    .isEquals();
        } else {
            return false;
        }
    }

    @Override
    public int hashCode() {
        return new HashCodeBuilder()
                .append(cloudProcess)
                .append(toCloudComputer)
                .toHashCode();
    }

Notice that it checks if the other move is an instance of the same move type. This instanceof check is important because a move are compared to a move of another move type. For example a ChangeMove and SwapMove are compared.

7.4. Generating custom moves

Now, let’s generate instances of this custom Move class. There are 2 ways:

7.4.1. MoveListFactory: the easy way to generate custom moves

The easiest way to generate custom moves is by implementing the interface MoveListFactory:

public interface MoveListFactory<Solution_> {

    List<Move> createMoveList(Solution_ solution);

}

For example:

public class CloudComputerChangeMoveFactory implements MoveListFactory<CloudBalance> {

    @Override
    public List<CloudComputerChangeMove> createMoveList(CloudBalance cloudBalance) {
        List<CloudComputerChangeMove> moveList = new ArrayList<>();
        List<CloudComputer> cloudComputerList = cloudBalance.getComputerList();
        for (CloudProcess cloudProcess : cloudBalance.getProcessList()) {
            for (CloudComputer cloudComputer : cloudComputerList) {
                moveList.add(new CloudComputerChangeMove(cloudProcess, cloudComputer));
            }
        }
        return moveList;
    }

}

Simple configuration (which can be nested in a unionMoveSelector just like any other MoveSelector):

    <moveListFactory>
      <moveListFactoryClass>org.optaplanner.examples.cloudbalancing.optional.solver.move.CloudComputerChangeMoveFactory</moveListFactoryClass>
    </moveListFactory>

Advanced configuration:

    <moveListFactory>
      ... <!-- Normal moveSelector properties -->
      <moveListFactoryClass>org.optaplanner.examples.cloudbalancing.optional.solver.move.CloudComputerChangeMoveFactory</moveListFactoryClass>
      <moveListFactoryCustomProperties>
        ...<!-- Custom properties -->
      </moveListFactoryCustomProperties>
    </moveListFactory>

Because the MoveListFactory generates all moves at once in a List<Move>, it does not support cacheType JUST_IN_TIME. Therefore, moveListFactory uses cacheType STEP by default and it scales badly.

To configure values of a MoveListFactory dynamically in the solver configuration (so the Benchmarker can tweak those parameters), add the moveListFactoryCustomProperties element and use custom properties.

A custom MoveListFactory implementation must ensure that it does not move pinned entities.

7.4.2. MoveIteratorFactory: generate Custom moves just in time

Use this advanced form to generate custom moves Just In Time by implementing the MoveIteratorFactory interface:

public interface MoveIteratorFactory<Solution_> {

    long getSize(ScoreDirector<Solution_> scoreDirector);

    Iterator<Move> createOriginalMoveIterator(ScoreDirector<Solution_> scoreDirector);

    Iterator<Move> createRandomMoveIterator(ScoreDirector<Solution_> scoreDirector, Random workingRandom);

}

The getSize() method must return an estimation of the size. It doesn’t need to be correct, but it’s better too big than too small. The createOriginalMoveIterator method is called if the selectionOrder is ORIGINAL or if it is cached. The createRandomMoveIterator method is called for selectionOrder RANDOM combined with cacheType JUST_IN_TIME.

Don’t create a collection (array, list, set or map) of Moves when creating the Iterator<Move>: the whole purpose of MoveIteratorFactory over MoveListFactory is to create a Move just in time in a custom Iterator.next().

Simple configuration (which can be nested in a unionMoveSelector just like any other MoveSelector):

    <moveIteratorFactory>
      <moveIteratorFactoryClass>...</moveIteratorFactoryClass>
    </moveIteratorFactory>

Advanced configuration:

    <moveIteratorFactory>
      ... <!-- Normal moveSelector properties -->
      <moveIteratorFactoryClass>...</moveIteratorFactoryClass>
      <moveIteratorFactoryCustomProperties>
        ...<!-- Custom properties -->
      </moveIteratorFactoryCustomProperties>
    </moveIteratorFactory>

To configure values of a MoveIteratorFactory dynamically in the solver configuration (so the Benchmarker can tweak those parameters), add the moveIteratorFactoryCustomProperties element and use custom properties.

A custom MoveIteratorFactory implementation must ensure that it does not move pinned entities.