Abstract
Particle Swarm Optimization is a promising evolutionary optimization algorithm. In this paper, an improved hybrid multi-objective particle swarm optimization algorithm (IHMOPSO) is proposed. IHMOPSO uses orthogonal design to initialize population, selects global optimal position from Pareto set. Apply mutation, cross operation and evolutionary selection, and uses two ways to update the position and velocity of particles. Experimental results on many well-known benchmark optimization problems have shown that IHMOPSO is effective and efficient.
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Zhou, Z., Dai, G., Fang, P., Chen, F., Tan, Y. (2008). An Improved Hybrid Multi-objective Particle Swarm Optimization Algorithm. In: Kang, L., Cai, Z., Yan, X., Liu, Y. (eds) Advances in Computation and Intelligence. ISICA 2008. Lecture Notes in Computer Science, vol 5370. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-92137-0_20
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DOI: https://doi.org/10.1007/978-3-540-92137-0_20
Publisher Name: Springer, Berlin, Heidelberg
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