A Knee Point-Driven Evolutionary Algorithm for Many-Objective Optimization

Evolutionary algorithms (EAs) have shown to be promising in solving many-objective optimization problems (MaOPs), where the performance of these algorithms heavily depends on whether solutions that can accelerate convergence toward the Pareto front and maintaining a high degree of diversity will be...

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Veröffentlicht in:IEEE transactions on evolutionary computation Jg. 19; H. 6; S. 761 - 776
Hauptverfasser: Zhang, Xingyi, Tian, Ye, Jin, Yaochu
Format: Journal Article
Sprache:Englisch
Veröffentlicht: New York IEEE 01.12.2015
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Schlagworte:
Algorithms
Convergence
diversity
Evolutionary computation
Evolutionary multi-objective optimization
hypervolume
knee point
many-objective optimization
Measurement
Optimization
Pareto optimization
Production scheduling
Sociology
evolutionary multiobjective optimization
many-objective optimization
diversity
hypervolume (HV)
knee point
Convergence
ISSN:1089-778X, 1941-0026
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Zusammenfassung:Evolutionary algorithms (EAs) have shown to be promising in solving many-objective optimization problems (MaOPs), where the performance of these algorithms heavily depends on whether solutions that can accelerate convergence toward the Pareto front and maintaining a high degree of diversity will be selected from a set of nondominated solutions. In this paper, we propose a knee point-driven EA to solve MaOPs. Our basic idea is that knee points are naturally most preferred among nondominated solutions if no explicit user preferences are given. A bias toward the knee points in the nondominated solutions in the current population is shown to be an approximation of a bias toward a large hypervolume, thereby enhancing the convergence performance in many-objective optimization. In addition, as at most one solution will be identified as a knee point inside the neighborhood of each solution in the nondominated front, no additional diversity maintenance mechanisms need to be introduced in the proposed algorithm, considerably reducing the computational complexity compared to many existing multiobjective EAs for many-objective optimization. Experimental results on 16 test problems demonstrate the competitiveness of the proposed algorithm in terms of both solution quality and computational efficiency.
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ISSN:1089-778X
1941-0026
DOI:10.1109/TEVC.2014.2378512