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Self-adaptive differential evolution algorithm with α-constrained-domination principle for constrained multi-objective optimization

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Abstract

Real-world problems are inherently constrained optimization problems often with multiple conflicting objectives. To solve such constrained multi-objective problems effectively, in this paper, we put forward a new approach which integrates self-adaptive differential evolution algorithm with α-constrained-domination principle, named SADE-αCD. In SADE-αCD, the trial vector generation strategies and the DE parameters are gradually self-adjusted adaptively based on the knowledge learnt from the previous searches in generating improved solutions. Furthermore, by incorporating domination principle into α-constrained method, α-constrained-domination principle is proposed to handle constraints in multi-objective problems. The advantageous performance of SADE-αCD is validated by comparisons with non-dominated sorting genetic algorithm-II, a representative of state-of-the-art in multi-objective evolutionary algorithms, and constrained multi-objective differential evolution, over fourteen test problems and four well-known constrained multi-objective engineering design problems. The performance indicators show that SADE-αCD is an effective approach to solving constrained multi-objective problems, which is basically enabled by the integration of self-adaptive strategies and α-constrained-domination principle.

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Notes

  1. The source code can be downloaded from the author’s homepage.

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Acknowledgments

Authors would like to express their sincere thanks to the reviewers for their valuable suggestions and comments, and Dr. P.N. Suganthan for providing the source codes of CMODE. This work was supported by Major State Basic Research Development Program of China (973 Program: 2012CB720500), National Natural Science Foundation of China (Key Program: 61134007), Major State Basic Research Development Program of Shanghai (10JC1403500), New Teacher Fund Program of Specialized Research Fund for the Doctoral Program of Higher Education (No. 200802511011), Shanghai Leading Academic Discipline Project (No. B504).

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Authors and Affiliations

  1. Key Laboratory of Advanced Control and Optimization for Chemical Processes, Ministry of Education, East China University of Science and Technology, Shanghai, 200237, China

    Feng Qian, Bin Xu & Rongbin Qi

  2. Department of Computer, Communications and Interactive systems, School of Engineering and Built Environment, Glasgow Caledonian University, Glasgow, UK

    Huaglory Tianfield

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  1. Feng Qian
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Qian, F., Xu, B., Qi, R. et al. Self-adaptive differential evolution algorithm with α-constrained-domination principle for constrained multi-objective optimization. Soft Comput 16, 1353–1372 (2012). https://doi.org/10.1007/s00500-012-0816-6

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