Abstract
Multi-Objective Particle Swarm Optimizers (MOPSOs) easily converge to a false Pareto front. In this paper, we proposed a hybrid algorithm of MOPSO with evolutionary programming (denoted as EPMOPSO) for solving MOPs. In EPMOPSO, the neighborhood of each particle is dynamically constructed, and the velocity of each particle is adjusted by all particles in its neighborhood including itself, the best performing particle in the swarm and the elite group that is evolved using evolutionary programming. Simulation results show that EPMOPSO is able to find a much better spread of solutions and has faster convergence to true Pareto-optimal front compared with five state-of-the-art MOPSOs.
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Shao, Z., Liu, Y., Dong, S. (2010). Multi-Objective PSO Based on Evolutionary Programming. In: Huang, DS., Zhao, Z., Bevilacqua, V., Figueroa, J.C. (eds) Advanced Intelligent Computing Theories and Applications. ICIC 2010. Lecture Notes in Computer Science, vol 6215. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-14922-1_75
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DOI: https://doi.org/10.1007/978-3-642-14922-1_75
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