Abstract
In this paper, a novel multi-objective particle swarm optimization algorithm is proposed based on decomposing the objective space into a number of subregions and optimizing them simultaneously. The subregion strategy has two very desirable properties with regard to multi-objective optimization. One advantage is that the local best in the subregion can effectively guide the particles to Pareto front combining with global best. The other is that it has a better performance on the convergence and diversity of solutions. Additionally, this paper applies min-max strategy with determined weight as fitness functions to multi-objective particle swarm optimization, and there is no additional clustering or niching technique needed. In order to demonstrate the performance of the algorithm, it is compared with MOPSO and DMS-MO-PSO. The results indicate that proposed algorithm is efficient.
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Zhao, Y., Liu, HL. (2013). Multi-Objective Particle Swarm Optimization Algorithm Based on Population Decomposition. In: Yin, H., et al. Intelligent Data Engineering and Automated Learning – IDEAL 2013. IDEAL 2013. Lecture Notes in Computer Science, vol 8206. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-41278-3_56
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DOI: https://doi.org/10.1007/978-3-642-41278-3_56
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