Abstract
Binary PSO algorithms are extensions of the PSO algorithm that enjoy some of the social intelligence properties of the original algorithm. The intensive local search ability is one of the most important characteristics of PSO. In this paper, we argue that, when evaluating binary PSO algorithms against common real-value benchmark problems—a common practice in the literature—the representation of the search space can have a significant effect on the results. For this purpose we propose the use of reflected binary code, which is a minimal change ordering representation for mapping a binary genotype space to a real phenotype space, while preserving the notion of locality in the phenotype space.
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References
Kennedy, J., Eberhart, R.C.: Particle swarm optimization. In: Proceedings of the 1995 IEEE International Conference on Neural Networks, Perth, Australia, vol. 4, pp. 1942–1948. IEEE Service Center, Piscataway (1995)
Kennedy, J., Eberhart, R.C.: A discrete binary version of the particle swarm algorithm. In: Proceedings of the IEEE International Conference on Systems, Man, and Cybernetics, Washington, DC, USA, vol. 5, pp. 4104–4108, IEEE Computer Society, October 1997
Zhen, L., Wang, L., Wang, X., Huang, Z.: A novel PSO-inspired probability-based binary optimization algorithm. In: 2008 International Symposium on Information Science and Engineering, vol. 2, pp. 248–251, December 2008
Chakraborty, U.K., Janikow, C.Z.: An analysis of gray versus binary encoding in genetic search. Inf. Sci. 156(3–4), 253–269 (2003)
Shi, Y., Eberhart, R.C.: A modified particle swarm optimizer. In: Proceedings of IEEE International Conference on Evolutionary Computation, Washington, DC, USA, pp. 69–73. IEEE Computer Society, May 1998
Kennedy, J., Eberhart, R.C.: A discrete binary version of the particle swarm algorithm. In: 1997 IEEE International Conference on Systems, Man, and Cybernetics, Computational Cybernetics and Simulation. vol. 5, pp. 4104–4108, October 1997
Khanesar, M.A., Teshnehlab, M., Shoorehdeli, M.A.: A novel binary particle swarm optimization. In: 2007 Mediterranean Conference on Control Automation, pp. 1–6, June 2007
Khanesar, M.A.: Binary particle swarm optimization (2013)
Gray, F.: Pulse code communication. US Patent 2,632,058 (1953)
Reingold, E.M., Nievergelt, J., Deo, N.: Combinatorial Algorithms: Theory and Practice. Prentice Hall College Div, Englewood Cliffs (1977)
IEEE: IEEE standard for floating-point arithmetic. Standard, IEEE Computer Society, August 2008
Kreher, D.L., Stinson, D.R.: Combinatorial Algorithms: Generation, Enumeration, and Search. CRC Press, Boca Raton (1998)
Suganthan, P.N., et al.: Problem definitions and evaluation criteria for the CEC 2005 special session on real-parameter optimization. Technical report, Nanyang Technological University, Singapore (2005)
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Yamada, S., Neshatian, K. (2018). Improving Representation in Benchmarking of Binary PSO Algorithms. In: Mitrovic, T., Xue, B., Li, X. (eds) AI 2018: Advances in Artificial Intelligence. AI 2018. Lecture Notes in Computer Science(), vol 11320. Springer, Cham. https://doi.org/10.1007/978-3-030-03991-2_35
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DOI: https://doi.org/10.1007/978-3-030-03991-2_35
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