期刊名称：Journal of Theoretical and Applied Computer Science
印刷版ISSN：2299-2634
电子版ISSN：2300-5653
出版年度：2013
卷号：7
期号：1
页码：46-55
出版社：Polska Akademia Nauk * Oddzial w Gdansku, Komisja Informatyki,Polish Academy of Sciences, Gdansk Branch, Computer Science Commission
摘要：We compare six metaheuristic optimization algorithms applied to solving the travelling salesman problem. We focus on three classical approaches: genetic algorithms, simulated annealing and tabu search, and compare them with three recently developed ones: quantum annealing, particle swarm optimization and harmony search. On top of that we compare all results with those obtained with a greedy 2-opt interchange algorithm. We are interested in short-term performance of the algorithms and use three criteria to evaluate them: solution quality, standard deviation of results and time needed to reach the optimum. Following the results from simulation experiments we conclude that simulated annealing and tabu search outperform newly developed approaches in short simulation runs with respect to all three criteria. Simulated annealing finds best solutions, yet tabu search has lower variance of results and converges faster.
关键词：metaheuristic algorithms; travelling salesman problem
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