Abstract
In this study, we present a novel particle swarm optimizer, called Gender-Hierarchy Based Particle Swarm Optimizer (GH-PSO), to handle multi-objective optimization problems. By employing the concepts of gender and hierarchy to particles, both the exploration ability and the exploitation skill are extended. In order to maintain an uniform distribution of non-dominated solutions, a novel proposal, called Rectilinear Distance based Selection and Replacement (RDSR), is also proposed. The proposed algorithm is validated by using several benchmark functions and metrics. The results show that the proposed algorithm outperforms over MOPSO, NSGA-II and PAES-II.
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Wei, W., Zhang, W., Jiang, Y., Li, H. (2012). Handling Multi-optimization with Gender-Hierarchy Based Particle Swarm Optimizer. In: Tan, Y., Shi, Y., Ji, Z. (eds) Advances in Swarm Intelligence. ICSI 2012. Lecture Notes in Computer Science, vol 7331. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-30976-2_12
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DOI: https://doi.org/10.1007/978-3-642-30976-2_12
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