Abstract
The search ability of an algorithm in terms of convergence and diversity can be improved with the help of corner points. A corner point-based algorithm (CPA) based on a differential evolution (DE) algorithm is proposed to solve constrained multi-objective optimization problems. The evolutionary algorithm consists of two stages. The first stage is to find corner points by the proposed method. The second stage is to approach the real Pareto front. A novel diversity and convergence mechanism is implemented in the second stage. The performance of the proposed algorithm is evaluated on nineteen test functions. Compared with the constrained handling techniques and latest optimization algorithms, the numerical results have indicated that the proposed algorithm is effective. At last, the algorithm is used to solve resource schedule in emergency management to further validate its effectiveness.
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Acknowledgements
The authors would like to thank the editor and the anonymous reviewers for their very helpful suggestions.
This study was funded by China Natural Science Foundation (grant number. 71503134, 91546117, 71373131), Key Project of National Social and Scientific Fund Program (16ZDA047), Priority Academic Program Development of Jiangsu Higher Education Institutions (PAPD) and philosophy and Social Sciences in Universities of Jiangsu (grant number. 2016SJB630016).
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Yu, X., Lu, Y. A corner point-based algorithm to solve constrained multi-objective optimization problems. Appl Intell 48, 3019–3037 (2018). https://doi.org/10.1007/s10489-017-1126-6
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DOI: https://doi.org/10.1007/s10489-017-1126-6