Title | ||
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A novel two-archive matching-based algorithm for multi- and many-objective optimization. |
Abstract | ||
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In evolutionary multi-objective optimization, it is crucial for the evolutionary algorithm to maintain a good balance between convergence and diversity. The recently proposed Two_Arch2 algorithm provides a new perspective to solve this problem. However, due to the properties of the mating mechanism and the limitations of the distance-based diversity maintenance scheme, both the computational complexity and the diversity face great challenges as the number of objectives increases. In this paper, we propose an improved Two-Archive algorithm for both multi- and many-objective optimization, aiming at further promoting the balance between convergence and diversity. In the proposed algorithm, we introduce a decomposition idea into the mating pool of the convergence archive, which increases the number of favorable solutions and reduces the computational complexity. At the same time, we apply a penalty angle-based selection scheme to the diversity archive, which effectively maintains the population diversity. The effectiveness of the proposed algorithm is compared with five state-of-the-art multi-objective evolutionary algorithms on a variety of benchmark problems. The experimental results demonstrate that the proposed algorithm has highly competitive performance on both multi- and many-objective optimization problems—in particular, remedying problems of Two_Arch2. |
Year | DOI | Venue |
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2019 | 10.1016/j.ins.2019.05.028 | Information Sciences |
Keywords | Field | DocType |
Evolutionary algorithm,Penalty angle,Multi-objective optimization,Many-objective optimization,Two-archive algorithm | Convergence (routing),Evolutionary algorithm,Algorithm,Population diversity,Diversity maintenance,Mating pool,Mathematics,Computational complexity theory | Journal |
Volume | ISSN | Citations |
497 | 0020-0255 | 0 |
PageRank | References | Authors |
0.34 | 0 | 3 |
Name | Order | Citations | PageRank |
---|---|---|---|
Chunteng Bao | 1 | 9 | 1.18 |
Lihong Xu | 2 | 344 | 36.70 |
Erik Goodman | 3 | 145 | 15.19 |