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file | bestInsert.m [code] |
| Identifies the best insertion in a tour for the TSP.
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file | getTourList.m [code] |
| Transforms a tour stored as a sequence of cities into a list of successors.
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file | getTourSequence.m [code] |
| Transform tour stored as list of successor, into a sequence of cities.
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file | insertCity.m [code] |
| Inserts a city in a tour for the TSP.
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file | knapsackGreedy.m [code] |
| Solves the knapsack problem using the greedy heuristic described in section 27.1.1 of [1].
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file | ksLocalSearchDeterministic.m [code] |
| Algorithm 27.3: local search for the knapsack problem.
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file | ksLocalSearchRandom.m [code] |
| Implementation of a variant of the local search algorithm with random neighbors for the knapsack problen.
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file | ksRandomNeighbor.m [code] |
| Algorithm 27.5: neighborhood for the knapsack problem.
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file | ksSimulatedAnnealing.m [code] |
| Algorithm 27.7: simulated annealing for the knapsack problem.
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file | ksVns.m [code] |
| Algorithm 27.5: VNS for the knapsack problem.
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file | randomFromAtoB.m [code] |
| Compute a random integer between A and B.
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file | run2702.m [code] |
| Runs example 27.2 of [1], solving the problem using the greedy heuristic.
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file | run2702greedy.m [code] |
| Runs example 27.2 of [1], solving the problem using the greedy heuristic.
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file | run2702localSearchDeterministic.m [code] |
| Runs example 27.2 of [1], solving the problem using the local search method (Table 27.6)
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file | run2702localSearchRandom.m [code] |
| Runs example 27.2 of [1], solving the problem using the local search method with random neighbors.
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file | run2702simAnn.m [code] |
| Runs example 27.2 of [1], solving the problem using the simulated annealing method (see Section 27.4.1 of [1]).
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file | run2702vns.m [code] |
| Runs example 27.2 of [1], solving the problem using the VNS method.
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file | run2703insertion.m [code] |
| Runs example 27.3 of [1], solving the problem using the greedy heuristic (Algorithm 27.2, Table 27.2, Figure 27.4)
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file | run2703localSearch.m [code] |
| Runs Algorithm 27.3 on example 27.3 of [1], solving the TSP with local search (Figure 27.11, Table 27.7, Figure 27.12)
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file | run2703nearestNeighbor.m [code] |
| Runs example 27.3 of [1], solving the problem using the greedy heuristic (Algorithm 27.1)
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file | run2703nearestNeighrbor.m [code] |
| Runs example 27.3 of [1], solving the problem using the greedy heuristic.
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file | run2703simAnn.m [code] |
| Runs example 27.3 of [1], solving the problem using simmulated annealing (Section 27.4.2 of [1] ).
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file | run2703vns.m [code] |
| Runs example 27.3 of [1], solving the problem using VNS (Section 27.3.2 of [1] ).
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file | subtourLength.m [code] |
| Calculate the length of a subtour of the TSP.
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file | tspInsertion.m [code] |
| Algorithm 27.2: insertion heuristic for TSP.
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file | tspInsertionLocalSearch.m [code] |
| Local search method for the TSP based on the insertion heuristic, as described in Section 27.3.2 of [1].
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file | tspLocalSearch.m [code] |
| Algorithm 27.3: local search for the TSP.
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file | tspNearestNeighbor.m [code] |
| Algorithm 27.1: nearest neighbor heuristic for TSP.
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file | tspSimulatedAnnealing.m [code] |
| Algorithm 27.7: simulated annealing for the TSP.
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file | tspTourLength.m [code] |
| Calculate the length of a tour of the TSP.
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file | tspVns.m [code] |
| Implements the VNS to solve the TSP, as described in Section 27.3.2 of [1].
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file | twoOpt.m [code] |
| Perform a 2-opt operation of a list of cities.
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file | twoOptNeighborhood.m [code] |
| Generate the 2-opt neightborhood for the TSP.
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file | twoOptRandomNeighbor.m [code] |
| Generate one random 2-opt neighbor for the TSP.
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