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An angle based constrained many-objective evolutionary algorithm

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Abstract

Having successfully handled many-objective optimization problems with box constraints only by using VaEA, a vector angle based many-objective evolutionary algorithm in our precursor study, this paper extended VaEA to solve generic constrained many-objective optimization problems. The proposed algorithm (denoted by CVaEA) differs from the original one mainly in the mating selection and the environmental selection, which are made suitable in the presence of infeasible solutions. Furthermore, we suggest a set of new constrained many-objective test problems which have different ranges of function values for all the objectives. Compared with normalized problems, this set of scaled ones is more applicable to test an algorithm’s performance. This is due to the nature property of practical problems being usually far from normalization. The proposed CVaEA was compared with two latest constrained many-objective optimization methods on the proposed test problems with up to 15 objectives, and on a constrained engineering problem from practice. It was shown by the simulation results that CVaEA could find a set of well converged and properly distributed solutions, and, compared with its competitors, obtained a better balance between convergence and diversity. This, and the original VaEA paper, together demonstrate the usefulness and efficiency of vector angle based algorithms for handling both constrained and unconstrained many-objective optimization problems.

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Acknowledgments

The authors are grateful to the anonymous reviewers for their insightful comments.

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Authors and Affiliations

  1. School of Data and Computer Science, Collaborative Innovation Center of High Performance Computing, Sun Yat-sen University, Guangzhou, 510006, People’s Republic of China

    Yi Xiang, Jing Peng, Yuren Zhou & Zefeng Chen

  2. Centre of Excellence for Research in Computational Intelligence and Applications (CERCIA), School of Computer Science, University of Birmingham, Birmingham, B15 2TT, UK

    Miqing Li

Authors
  1. Yi Xiang
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  2. Jing Peng
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  3. Yuren Zhou
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  4. Miqing Li
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  5. Zefeng Chen
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Corresponding author

Correspondence to Yuren Zhou.

Additional information

This paper is supported by the National Natural Science Foundation of China (Grant nos. 61472143 and 61673403), and the Scientific Research Special Plan of Guangzhou Science and Technology Programme (Grant no. 201607010045)

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Cite this article

Xiang, Y., Peng, J., Zhou, Y. et al. An angle based constrained many-objective evolutionary algorithm. Appl Intell 47, 705–720 (2017). https://doi.org/10.1007/s10489-017-0929-9

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