Journal
INFORMATION SCIENCES
Volume 422, Issue -, Pages 305-317Publisher
ELSEVIER SCIENCE INC
DOI: 10.1016/j.ins.2017.08.078
Keywords
Evolutionary computation; Multi-objective evolutionary algorithms; Many-objective optimization; Two archives; Aggregation-based method
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Funding
- China Scholarship Council [201306290083]
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In this paper, a novel two-archive method is proposed for solving many-objective optimization problems. Our aim is to exploit the advantages of using two separate archives to balance the convergence and diversity. To this end, two updating strategies based on the aggregation-based framework are presented and incorporated into the two-archive method. In addition, we further extend this method by eliminating the restricted neighbourhood models. The proposed algorithms have been tested extensively on a number of well-known benchmark problems with 3-20 objectives. Experimental results reveal that the proposed algorithms work well on the many-objective optimization problems with different characteristics. (C) 2017 Elsevier Inc. All rights reserved.
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