免疫非支配自适应粒子群多目标优化

doi:10.3969/j.issn.1001-2400.2010.05.013

Abstract

Abstract:

Immune nondominated adaptive particle swarm multi-objective optimization(INPSMO) is studied. The nondominated particle swarm is proposed to efficiently apply particle swarm optimization into solving multi-objective optimization problems. Meanwhile, currently discovered non-dominated solutions are utilized to dynamically and adaptively adjust the inertia weights, which is critical to the evolutionary process of particles. Besides, the clone selection principle in the artificial immune system is employed for particle proliferation, which is beneficial to maintaining population diversity. Compared with three state-of-the-art multi-objective algorithms, namely, NSGA-Ⅱ, SPEA2 and PESA-Ⅱ, INPSMO achieves comparable results in terms of convergence and diversity metrics. Better computational complexity is also obtained by INPSMO.

Key words: evolutionary computation, multi-objective optimization, artificial immune system, particle swarm optimization

MA Jing-jing;YANG Dong-dong;JIAO Li-cheng. Immune nondominated adaptive particle swarm multi-objective optimization[J].J4, 2010, 37(5): 846-851.

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Immune nondominated adaptive particle swarm multi-objective optimization

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