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| Preference-Inspired Co-evolutionary Algorithm Based on Hybrid Domination Strategy |
| WANG Liping1,2, DU Jiejie1,2, QIU Feiyue3, JIANG Bo3 |
1.College of Economics and Management, Zhejiang University of Technology, Hangzhou 310023
2.Institute of Information Intelligence and Decision Optimization, Zhejiang University of Technology, Hangzhou 310023
3.College of Education, Zhejiang University of Technology, Hangzhou 310023 |
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Abstract The preference-inspired co-evolutionary algorithm employing goal vectors can not identify the Pareto dominance relationship of candidate solutions at the same fitness level, and the obtained solutions are unevenly distributed along the Pareto front. Aiming at these problems, preference-inspired co-evolutionary algorithm based on hybrid domination strategy(E-PICEA-g) is proposed in this paper. Firstly, Pareto dominance sorting on population is conducted, and then the candidate solutions fitness values are calculated to reduce the proportion of non-dominated solutions in the population and increase the selection pressure. Meanwhile, the distance between candidate solutions and ideal point is considered to punish the candidate solutions at the same fitness level but far from the ideal point. Thus, the obtained solutions are made to distribute evenly along the Pareto optimal front. Experimental results on 12 multi-objective optimization functions demonstrate that the proposed algorithm acquires solutions with high quality on most of the test functions.
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Received: 23 March 2017
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| Fund:Supported by National Natural Science Foundation of China(No.61503340,61472366,61379077), Natural Science Foundation of Zhejiang Province(No.LY17F020022,LQ16F030008) |
About author:: (WANG Liping, born in 1964, Ph.D., professor. Her research interests include decision optimization and information intelligence.)
(DU Jiejie,born in 1992,master student. Her research interests include multi-objective optimization.)
(QIU Feiyue(Corresponding author), born in 1965, Ph.D., professor. His research interests include computational intelligence and deep learning.)
(JIANG Bo, born in 1985, Ph.D., associate professor. His research interests include optimization and control. |
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2017, Vol. 30 
