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The crowd framework for multiobjective particle swarm optimization

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Abstract

Multiobjective particle swarm optimization meets two difficulties—guiding the search towards the Pareto front and maintaining diversity of the obtained solutions—so a great number of improvements are possible. Our crowd framework systematically summarizes these improvements, extracts them into reusable strategies and categorizes them into modules by their optimization mechanisms. We introduce a number of new techniques within the modules. Strategies are compared first theoretically and then practically through amended ZDT series. We propose a sequence for module application based on the correlation between the modules. The resulting algorithms give incredible performance. Thus our crowd framework forms a new baseline for MOPSO.

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Authors and Affiliations

  1. Department of Computer Science and Engineering, Shanghai Jiao Tong University, 800 Dongchuan Road, Shanghai, 200240, China

    Heming Xu, Yinglin Wang & Xin Xu

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  1. Heming Xu
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  2. Yinglin Wang
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  3. Xin Xu
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Correspondence to Yinglin Wang.

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Xu, H., Wang, Y. & Xu, X. The crowd framework for multiobjective particle swarm optimization. Artif Intell Rev 42, 1095–1138 (2014). https://doi.org/10.1007/s10462-012-9347-x

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