Ensemble of many-objective evolutionary algorithms for many-objective problems

Abstract

The performance of most existing multiobjective evolutionary algorithms deteriorates severely in the face of many-objective problems. Many-objective optimization has been gaining increasing attention, and many new many-objective evolutionary algorithms (MaOEA) have recently been proposed. On the one hand, solution sets with totally different characteristics are obtained by different MaOEAs, since different MaOEAs have different convergence-diversity tradeoff relations. This may suggest the potential usefulness of ensemble approaches of different MaOEAs. On the other hand, the performance of MaOEAs may vary greatly from one problem to another, so that choosing the most appropriate MaOEA is often a non-trivial task. Hence, an MaOEA that performs generally well on a set of problems is often desirable. This study proposes an ensemble of MaOEAs (EMaOEA) for many-objective problems. When solving a single problem, EMaOEA invests its computational budget to its constituent MaOEAs, runs them in parallel and maintains interactions between them by a simple information sharing scheme. Experimental results on 80 benchmark problems have shown that, by integrating the advantages of different MaOEAs into one framework, EMaOEA not only provides practitioners a unified framework for solving their problem set, but also may lead to better performance than a single MaOEA.

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Acknowledgments

This work was supported in part by National Natural Science Foundation of China (61379060), National Social Science Foundation of China (14BXW031), Guangdong Natural Science Foundation (2014A030313609), Zhujiang New Star Program of Science and Technology in Guangzhou(2012J2200085), Excellent Young Teachers Training Program in Guangdong Colleges and Universities (Yqgdufe1404), and Program for Characteristic Innovation Talents of Guangdong (2014KTSCX127).

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Correspondence to Jiahai Wang.

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The authors declare that they have no conflict of interest. This research does not involve any human participant or animal and thus has no informed consent.

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Communicated by V. Loia.

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Zhou, Y., Wang, J., Chen, J. et al. Ensemble of many-objective evolutionary algorithms for many-objective problems. Soft Comput 21, 2407–2419 (2017). https://doi.org/10.1007/s00500-015-1955-3

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Keywords

  • Many-objective evolutionary algorithm
  • Ensemble
  • Algorithm portfolios
  • Many-objective optimization