A solver based on local search.
The goal of this repository is to provide a simple framework to quickly implement heuristic algorithms based on local search.
For complex and evolving problems, implementing and extending local search algorithms based on complex neighborhoods quickly becomes cumbersome and time consuming. This often makes them unsuitable for practical use. The algorithms of this repository are designed to get the best performances out of the simplest neighborhoods. Thus making them easier to implement and extend.
Still, more complex neighborhoods can be implemented if better performances are needed.
All algorithms require the Local Scheme to implement: GlobalCost, Solution, initial_solution, global_cost and local_search.
Implemented algorithms:
- Multi-start local search
-a restarting-local-search- Most basic algorithm, can be used as a baseline
- Iterated local search
-a iterated-local-search- Single solution algorithm
- Low implementation cost
- Good for very large problems
- Additional requirements:
Perturbation,perturbations,apply_perturbation
- Best first local search
-a best-first-local-search- Multi-solution algorithm
- Higher implementation cost
- Higher quality solutions
- Additional requirements:
CompactSolution,compact_solution_hasher,solution2compact,compact2solution,Perturbation,perturbation_hasher,perturbations,apply_perturbation
- Genetic local search
-a genetic-local-search- Population-based algorithm
- Good when the ruggedness of the landscape is high
- Additional requirements:
crossoveranddistance
A specific implementation is also available for sequencing problems. The neighborhoods, perturbations and crossovers are already implemented. It is only required to provide a void append(SequenceData&, ElementId) const method to use them.
Following the framework described in "A unified solution framework for multi-attribute vehicle routing problems" (Vidal et al., 2014) DOI , more efficient neighborhood explorations can be obtained by providing either a void concatenate(SequenceData&, const SequenceData&) const method or a GlobalCost global_cost_concatenate(SequenceData&, const SequenceData&) const method (when it is possible).
In case the GlobalCost global_cost_concatenate(SequenceData&, const SequenceData&) const method is provided, the Swap and Reverse neighborhoods won't take much avantage of it. Therefore, it might be better to disable these neighborhoods, in particular when optimizing a single sequence.
- Local search neighborhoods:
- Intra:
- Shift a block of
kconsecutive elements - Shift and reverse a block of
kconsecutive elements - Swap a block of
k1consecutive elements with another block ofk2consecutive elements - Reverse a block of consecutive elements (2-opt)
- Shift a block of
- Inter:
- Swap the tails of two sequences (2-opt*)
- Replace the tail of sequence
i1with the reversed head of sequencei2and replace the head of sequencei2with the reversed tail of sequencei1(reversed 2-opt*) - Shift a block of
kconsecutive elements from one sequence to another - Shift and reverse a block of
kconsecutive elements from one sequence to another - Swap a block of
k1consecutive elements from a sequence with another block ofk2consecutive elements from another sequence - Swap the sequences of two elements
j1andj2and try to place them at promising positions in their new sequences (swap*)
- Sub-sequences:
- Add an element into the solution
- Remove an element from the solution
- Replace an element from the solution by an element outside of the solution
- Modes:
- Shift a block of
kconsecutive elements and change the mode of the first one. - Swap the mode of element
j1with the mode of another elementj2from the same sequence - Swap an elemnt
j1with another elementj2from the same sequence and swap their modes - Increment the mode of element
j1and decrement the mode of elementj2from the same sequence
- Shift a block of
- Intra:
- Perturbations:
- Swap two blocks of consecutive elements (double-bridge) (single sequence only)
- Remove
kelements and re-insert them (ruin-and-recreate) - Force an element into the solution
- Crossover algorithms:
- Order crossover (OX): single or multiple sequences
- Similar job order crossover (SJOX): single sequence
- Similar block order crossover (SBOX): single sequence
- Maximal preservative crossover (MPX): single sequence
- Cycle crossover (CX): single sequence
- Selective route exchange crossover 1 (SREX1): multiple sequences
- Selective route exchange crossover 2 (SREX2): multiple sequences
Single machine scheduling problem with sequence-dependent setup times, total weighted tardiness
Permutation flow shop scheduling problem, total completion time
Permutation flow shop scheduling problem, total tardiness
Time-dependent orienteering problem
Distributed permutation flow shop scheduling problem, total completion time
Traveling salesman problem with release dates
Single machine batch scheduling problem, total weighted tardiness
Capacitated vehicle routing problem
Vehicle routing problem with time windows
Data can be downloaded from fontanf/orproblems
Multidimensional multiple-choice knapsack problem
- Straightforward example for a genetic local search: single neighborhood, basic operators
- Algorithm:
- Local search neighborhoods:
- Add item
jin the knapsack
- Add item
- Crossover algorithm
- Local search neighborhoods:
- Example which implements a problem specific acceleration strategy to compute the move costs
- Algorithm:
- Local search neighborhood: swap two assignments
- Perturbation: ejection chain
- Crossover algorithm: UX crossover
Knapsack problem with conflicts
- Example with multiple neigborhoods
- Algorithm:
- Local search neighborhoods:
- Move item
jin/out of the solution (and remove conflicting items) - Swap two non-conflicting items
- Remove one item and add two non-conflicting items from its neighbors
- Move item
- Perturbation: force item
jin/out of the solution - Crossover algorithm: double backbone-based crossover
- Local search neighborhoods:
Generalized assignment problem from fontanf/generalizedassignmentsolver
- Example which implements a generic strategy for very large problems to avoid recomputing moves which have not change since the last neighborhood exploration
- Algorithm:
- Local search neighborhoods: shift item
jto agenti - Perturbation: shift 8 random jobs
- Local search neighborhoods: shift item
Permutation flow shop scheduling problem, makespan
- This one is not considered as a sequencing problem since the dedicated acceleration strategy makes it possible to explore the neighborhoods more efficiently
- Algorithm:
- Local search neighborhood: move a block of
kconsecutive jobs,k = 1..4 - Perturbation: swap two blocks (double-bridge)
- Local search neighborhood: move a block of
Maximum-weight independent set problem and maximum-weight clique problem from fontanf/stablesolver
- Algorithm:
- Local search neighborhoods:
- Move vertex
vin/out of the solution (and remove conflicting vertices) - Remove one vertex and add two non-conflicting vertices from its neighbors
- Move vertex
- Perturbation: force vertex
vin/out of the solution
- Local search neighborhoods:
Compile:
cmake -S . -B build -DCMAKE_BUILD_TYPE=Release
cmake --build build --config Release --parallel
cmake --install build --config Release --prefix installDownload data:
python3 scripts/download_data.pyThen, examples can be executed as follows:
./install/bin/localsearchsolver_sequential_ordering --verbosity-level 1 --input ./data/sequential_ordering/soplib/R.200.100.1.sop --format soplib --algorithm best-first-local-search --maximum-number-of-nodes 100 --certificate solution.txt=======================================
LocalSearchSolver
=======================================
Problem
-------
Sequential ordering problem
Number of locations: 200
Local scheme parameters
-----------------------
Neighborhoods
Shift
Block maximum length: 8
Swap
Block maximum length: 2
Reverse: 0
Shift-reverse
Block maximum length: 3
Add/Remove: 0
Replace: 0
Perturbations
Double-bridge
Number of perturbations: 0
Ruin-and-recreate
Number of perturbations: 10
Number of elements removed: 4
Ruin random weight: 1
Ruin nearest weight: 0
Ruin adjacent string removal weight: 0
Maximum string cardinality: 10
Split rate: 0.5
Beta: 0.01
Recreate random: 0
Recreate best: 0
Force-add: 0
Crossovers
Order crossover: 0
Similar job order crossover: 0
Similar block order crossover: 0
Selective route exchange crossover 1: 0
Selective route exchange crossover 2: 0
Maximal preservative crossover: 0
Algorithm
---------
Best first local search
Parameters
----------
Time limit: inf
Messages
Verbosity level: 1
Standard output: 1
File path:
# streams: 0
Logger
Has logger: 0
Standard error: 0
File path:
Solution pool size: 1
Seed: 0
Goal:
Maximum number of nodes: 100
Time Value Comment
---- ----- -------
4.486 290, 0 s0 (t0)
7.116 272, 0 n2 d1 c2 (t0)
10.021 236, 0 n5 d2 c2 (t0)
31.258 224, 0 n34 d6 c5 (t0)
38.488 209, 0 n44 d7 c9 (t0)
39.214 206, 0 n45 d8 c0 (t0)
51.860 204, 0 n64 d11 c5 (t0)
53.334 202, 0 n66 d12 c1 (t0)
54.232 199, 0 n67 d13 c0 (t0)
70.541 194, 0 n93 d13 c6 (t0)
Algorithm statistics
--------------------
Value: 194, 0
Time (s): 74.0243
Number of nodes: 100
Local scheme statistics
-----------------------
General
Initial solution: 1 / 1.5102e-05s / 1.5102e-05s
Crossover time: 0 / 0s / -nans
Local search time: 101 / 74.0166s / 0.732838s
Local search iterations: 2839 / 28.1089
Temporary structures time: 0.0108234s
Neighborhoods
1-shift 634 / 290 / 45.7413% / 6.81606s
2-shift 652 / 302 / 46.319% / 6.83057s
3-shift 657 / 284 / 43.2268% / 6.93018s
4-shift 670 / 317 / 47.3134% / 6.9931s
5-shift 618 / 305 / 49.3528% / 6.38262s
6-shift 645 / 319 / 49.4574% / 6.69405s
7-shift 638 / 309 / 48.4326% / 6.50337s
8-shift 659 / 311 / 47.1927% / 6.78887s
(1,1)-swap 571 / 76 / 13.31% / 2.53698s
(2,1)-swap 601 / 108 / 17.97% / 5.02293s
(2,2)-swap 572 / 76 / 13.2867% / 2.43386s
2-shift-reverse 599 / 96 / 16.0267% / 5.4184s
3-shift-reverse 597 / 46 / 7.70519% / 4.66367s
Local scheme solution
---------------------
Sequence 0: 0 16 105 172 177 107 91 119 80 158 164 104 151 42 74 187 53 175 99 147 70 27 136 153 56 87 118 123 125 196 114 26 28 6 30 36 143 195 82 20 127 92 135 148 109 55 72 193 24 160 25 197 113 5 157 63 8 76 149 21 38 191 51 22 45 88 59 52 3 188 111 134 106 9 37 40 130 103 128 10 54 81 186 146 166 145 180 131 120 95 62 61 184 100 190 154 44 183 169 178 129 17 78 165 18 116 139 23 173 126 85 75 50 66 67 58 174 69 39 60 159 64 98 93 141 79 140 110 144 14 65 189 133 115 101 4 156 43 117 34 102 12 83 108 29 94 132 170 89 121 46 182 19 168 73 97 171 162 194 122 90 33 84 152 96 77 86 198 161 112 11 68 142 13 35 179 163 32 181 176 150 192 137 124 71 2 49 47 57 41 15 185 48 31 155 7 167 138 1 199
Cost: 194, 0
Total cost: 194, 0
Checker
-------
Number of Vertices: 200 / 200
Number of duplicates: 0
Number of precedence violations: 0
Feasible: 1
Total distance: 194
./install/bin/localsearchsolver_knapsack_with_conflicts --verbosity-level 1 --input ./data/knapsack_with_conflicts/bettinelli2017/sparse_corr/test_1000_1000_r0.001-0.dat --format bettinelli2017 --time-limit 5 --certificate solution.txt=======================================
LocalSearchSolver
=======================================
Problem
-------
Knapsack problem with conflicts
Number of items: 1000
Capacity: 1000
Number of conflicts: 499
Weight ratio: 50.703
Average # of conflicts: 0.499
Algorithm
---------
Best first local search
Parameters
----------
Time limit: 5
Messages
Verbosity level: 1
Standard output: 1
File path:
# streams: 0
Logger
Has logger: 0
Standard error: 0
File path:
Solution pool size: 1
Seed: 0
Goal:
Maximum number of nodes: -1
Time Value Comment
---- ----- -------
0.001 1310.000000 s0 (t0)
0.002 1320.000000 n1 d1 c1 (t0)
0.010 1330.000000 n4 d2 c3 (t0)
0.010 1340.000000 n5 d3 c3 (t0)
0.012 1350.000000 n7 d4 c4 (t0)
0.013 1360.000000 n8 d5 c5 (t0)
0.014 1370.000000 n9 d6 c2 (t0)
0.016 1380.000000 n11 d7 c8 (t0)
0.018 1390.000000 n15 d8 c12 (t0)
0.018 1410.000000 n16 d9 c11 (t0)
0.020 1420.000000 n17 d10 c12 (t0)
0.020 1430.000000 n18 d11 c13 (t0)
0.021 1440.000000 n19 d12 c13 (t0)
0.024 1450.000000 n23 d13 c17 (t0)
0.025 1460.000000 n24 d14 c18 (t0)
0.026 1470.000000 n25 d15 c18 (t0)
0.032 1480.000000 n31 d16 c26 (t0)
0.033 1500.000000 n32 d17 c26 (t0)
0.034 1510.000000 n33 d18 c26 (t0)
0.035 1530.000000 n35 d19 c28 (t0)
0.036 1540.000000 n36 d20 c27 (t0)
0.038 1550.000000 n37 d21 c29 (t0)
0.038 1560.000000 n38 d22 c26 (t0)
0.039 1570.000000 n39 d23 c33 (t0)
0.057 1580.000000 n54 d24 c48 (t0)
0.063 1600.000000 n60 d25 c53 (t0)
0.071 1610.000000 n68 d26 c62 (t0)
0.101 1620.000000 n96 d27 c89 (t0)
0.115 1640.000000 n110 d28 c-2 (t0)
0.118 1650.000000 n113 d29 c12 (t0)
0.122 1660.000000 n116 d30 c-2 (t0)
0.140 1670.000000 n130 d31 c26 (t0)
0.141 1680.000000 n131 d32 c28 (t0)
0.153 1690.000000 n143 d33 c-2 (t0)
0.162 1700.000000 n152 d34 c-2 (t0)
0.166 1710.000000 n155 d35 c51 (t0)
0.170 1720.000000 n159 d36 c54 (t0)
0.219 1730.000000 n191 d37 c84 (t0)
0.230 1750.000000 n202 d38 c-2 (t0)
0.233 1760.000000 n204 d39 c93 (t0)
0.258 1770.000000 n219 d40 c-2 (t0)
0.266 1780.000000 n224 d41 c-2 (t0)
0.289 1790.000000 n238 d42 c-2 (t0)
0.485 1800.000000 n349 d44 c-2 (t0)
0.542 1810.000000 n380 d45 c-2 (t0)
0.547 1820.000000 n384 d46 c78 (t0)
0.553 1830.000000 n389 d47 c83 (t0)
0.578 1840.000000 n408 d48 c-2 (t0)
0.585 1850.000000 n414 d49 c14 (t0)
0.668 1860.000000 n464 d50 c-2 (t0)
0.678 1870.000000 n470 d51 c67 (t0)
0.681 1880.000000 n472 d52 c66 (t0)
0.686 1890.000000 n477 d53 c-2 (t0)
0.692 1900.000000 n482 d54 c-2 (t0)
0.697 1910.000000 n486 d55 c-2 (t0)
0.731 1920.000000 n512 d56 c-2 (t0)
0.749 1930.000000 n526 d57 c-2 (t0)
0.752 1940.000000 n530 d58 c91 (t0)
0.754 1950.000000 n531 d59 c28 (t0)
0.766 1960.000000 n540 d60 c37 (t0)
0.767 1970.000000 n541 d61 c37 (t0)
0.849 1980.000000 n591 d62 c-2 (t0)
0.869 1990.000000 n601 d63 c96 (t0)
1.204 2000.000000 n756 d65 c-2 (t0)
1.215 2010.000000 n763 d66 c-2 (t0)
1.287 2020.000000 n797 d67 c91 (t0)
1.488 2030.000000 n883 d68 c-2 (t0)
1.599 2040.000000 n936 d69 c-2 (t0)
1.649 2050.000000 n963 d70 c-2 (t0)
1.658 2060.000000 n968 d71 c66 (t0)
1.680 2070.000000 n977 d72 c-2 (t0)
1.682 2080.000000 n978 d73 c69 (t0)
1.689 2090.000000 n982 d74 c-2 (t0)
1.695 2100.000000 n984 d75 c74 (t0)
1.701 2110.000000 n988 d76 c75 (t0)
1.711 2120.000000 n996 d77 c85 (t0)
1.755 2130.000000 n1017 d78 c115 (t0)
1.756 2140.000000 n1018 d79 c114 (t0)
1.771 2150.000000 n1026 d80 c124 (t0)
1.787 2160.000000 n1035 d81 c133 (t0)
1.848 2170.000000 n1065 d82 c165 (t0)
2.001 2180.000000 n1149 d83 c247 (t0)
2.006 2190.000000 n1152 d84 c248 (t0)
2.007 2200.000000 n1153 d85 c246 (t0)
2.036 2210.000000 n1171 d86 c268 (t0)
2.044 2220.000000 n1175 d87 c265 (t0)
2.046 2230.000000 n1177 d88 c271 (t0)
2.063 2240.000000 n1187 d89 c280 (t0)
2.064 2250.000000 n1188 d90 c279 (t0)
2.072 2260.000000 n1192 d91 c284 (t0)
2.349 2270.000000 n1328 d92 c424 (t0)
2.361 2280.000000 n1336 d93 c429 (t0)
2.381 2290.000000 n1348 d94 c444 (t0)
2.411 2300.000000 n1367 d95 c462 (t0)
2.414 2310.000000 n1368 d96 c457 (t0)
3.451 2320.000000 n1811 d97 c-2 (t0)
3.521 2330.000000 n1849 d98 c445 (t0)
Algorithm statistics
--------------------
Value: 2330.000000
Time (s): 5.00157
Number of nodes: 2484
Local scheme statistics
-----------------------
Toggle: 14746 / 12232 / 82.9513%
Swap: 14556 / 12070 / 82.9211%
(2-1)-swap: 10738 / 13 / 0.121065%
Checker
-------
Number of Items: 133 / 1000
Number of duplicates: 0
Number of conflict violations: 0
Weight: 1000 / 1000
Feasible: 1
Profit: 2330
See examples.
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