Abstract
List-based simulated annealing (LBSA) algorithm is a novel simulated annealing algorithm where list-based cooling scheme is used to control the change of parameter temperature. Aiming to improve the efficiency of the LBSA algorithm for large-scale optimization problems, this paper proposes an enhanced LBSA (ELBSA) algorithm for solving large-scale traveling salesman problem (TSP). The ELBSA algorithm can drive more sampling at more suitable temperatures and from more promising neighborhoods. Specifically, heuristic augmented sampling strategy is used to ensure that more neighbors are from promising neighborhoods, systematic selection strategy is proposed to guarantee that each component of the current solution has a chance to be improved, and variable Markov chain length (VMCL), based on arithmetic sequence, is used to sample more neighbors at more suitable temperatures. Extensive experiments were performed to show the contribution of the heuristic augmented sampling strategy, and to verify the advantage of using systematic selection and VMCL. Comparative experiments, which were conducted on a wide range of large-scale TSP instances, show that the ELBSA algorithm is better than or competitive with most other state-of-the-art metaheuristics.
Highlights
Simulated annealing (SA) algorithm [1], [2] is a typical iterative metaheuristic with an explicit strategy to escape from local optima by allowing hill-climbing moves
The results of the Wilcoxon signed ranks test with a 0.05 significance level show that, except for the effective heuristics for ant colony optimization (ESACO), S-PSOi, and modified ant system (MAS) algorithms, the enhanced LBSA (ELBSA) algorithm is significantly better than the other metaheuristics, where the p-value is highlighted in bold
The proposed ELBSA algorithm was compared with 32 state-ofthe-art metaheuristics on a wide range of large-scale traveling salesman problem (TSP) instances
Summary
Simulated annealing (SA) algorithm [1], [2] is a typical iterative metaheuristic with an explicit strategy to escape from local optima by allowing hill-climbing moves. The random selection strategy, which is used by the LBSA algorithm to select the solution component to be replaced, may deteriorate its efficiency. Aiming to tackle those shortcomings, this paper proposes an enhanced list-based SA (ELBSA) algorithm. L. Wang et al.: ELBSA Algorithm for Large-scale TSP with heuristic augmented instance-based sampling strategy for the TSP. Comparative experiments, which were carried out on a wide range of large-scale benchmark TSP instances, show that the proposed algorithm is better than or competitive with most other state-of-the-art metaheuristics.
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