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A Novel Particle Swarm Optimization With Genetic Operator and Its Application to TSP

A Novel Particle Swarm Optimization With Genetic Operator and Its Application to TSP

Bo Wei, Ying Xing, Xuewen Xia, Ling Gui
Copyright: © 2021 |Volume: 15 |Issue: 4 |Pages: 17
ISSN: 1557-3958|EISSN: 1557-3966|EISBN13: 9781799859857|DOI: 10.4018/IJCINI.20211001.oa31
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MLA

Wei, Bo, et al. "A Novel Particle Swarm Optimization With Genetic Operator and Its Application to TSP." IJCINI vol.15, no.4 2021: pp.1-17. http://doi.org/10.4018/IJCINI.20211001.oa31

APA

Wei, B., Xing, Y., Xia, X., & Gui, L. (2021). A Novel Particle Swarm Optimization With Genetic Operator and Its Application to TSP. International Journal of Cognitive Informatics and Natural Intelligence (IJCINI), 15(4), 1-17. http://doi.org/10.4018/IJCINI.20211001.oa31

Chicago

Wei, Bo, et al. "A Novel Particle Swarm Optimization With Genetic Operator and Its Application to TSP," International Journal of Cognitive Informatics and Natural Intelligence (IJCINI) 15, no.4: 1-17. http://doi.org/10.4018/IJCINI.20211001.oa31

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Abstract

To solve some problems of particle swarm optimization, such as the premature convergence and falling into a sub-optimal solution easily, we introduce the probability initialization strategy and genetic operator into the particle swarm optimization algorithm. Based on the hybrid strategies, we propose a improved hybrid particle swarm optimization, namely IHPSO, for solving the traveling salesman problem. In the IHPSO algorithm, the probability strategy is utilized into population initialization. It can save much more computing resources during the iteration procedure of the algorithm. Furthermore, genetic operators, including two kinds of crossover operator and a directional mutation operator, are used for improving the algorithm’s convergence accuracy and population diversity. At last, the proposed method is benchmarked on 9 benchmark problems in TSPLIB and the results are compared with 4 competitors. From the results, it is observed that the proposed approach significantly outperforms others on most the 9 datasets.