Abstract
This paper presents a novel approach of set-based particle swarm optimization with memory state for solving traveling salesman problem. Particle swarm optimization achieves the social model of bird flocking and fish schooling and solves continuous optimization problem. Set-based particle swarm optimization functions in discrete space by using a set and solves combinatorial optimization problem with successfully applying to the large-scale problem. Our approach selects the best position among different positions from the current generation for creating a solution according to a velocity. In order to show the effectiveness of our approach, numerical experiments are presented for traveling salesman problem compared to existing algorithms.
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References
Kennedy, J., Eberhart, R.: Particle swarm optimization. In: Proceedings of IEEE International Conference on Neural Networks, pp. 1942–1948 (1995)
Chatterjee, A., Pulasinghe, K., Watanabe, K.: A particle-swarm-optimized fuzzy-neural network for voice-controlled robot systems. IEEE Trans. Ind. Electron. 52, 1478–1489 (2005)
Yoshida, H., Kawata, K., Fukuyama, Y.: A particle swarm optimization for reactive power and voltage control considering voltage security assessment. IEEE Trans. Power Syst. 15, 1232–1239 (2000)
Kennedy, J., Eberhart, R.: A discrete binary version of the particle swarm algorithm. In: Proceedings of IEEE International Conference on Systems, Man, and Cybernetics, pp. 4104–4109 (1997)
Ji, Z., Tian, T., He, S.: A memory binary particle swarm optimization. In: Proceedings of IEEE International Conference on Evolutionary Computation, pp. 1–5 (2012)
Chen, W., Zhang, J., Chung, H., Zhong, W., Wu, W., Shi, Y.: A novel set-based particle swarm optimization method for discrete optimization problems. IEEE Trans. Evol. Comput. 14, 278–299 (2010)
Stützle, T., Hoos, H.: Max-min ant system. Gener. Comput. Syst. 16, 889–914 (2000)
Hino, T., Ito, S., Liu, T., Maeda, M.: Set-based particle swarm optimization with status memory for knapsack problem. Artif. Life Robot. 21, 98–105 (2016)
Liang, J., Qin, A., Suganthan, P.: Comprehensive learning particle swarm optimizer for global optimization of multimodal functions. IEEE Trans. Evol. Comput. 10, 281–295 (2006)
Reinelt, G.: TSPLIB. http://comopt.ifi.uni-heidelberg.de/software/TSPLIB95/tsp/
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Maeda, M., Hino, T. (2017). A Novel Approach of Set-Based Particle Swarm Optimization with Memory State. In: Phon-Amnuaisuk, S., Ang, SP., Lee, SY. (eds) Multi-disciplinary Trends in Artificial Intelligence. MIWAI 2017. Lecture Notes in Computer Science(), vol 10607. Springer, Cham. https://doi.org/10.1007/978-3-319-69456-6_36
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DOI: https://doi.org/10.1007/978-3-319-69456-6_36
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