For many-objective optimization problems (MaOPs), how to get a set of solutions with good convergence and diversity is a difficult and challenging work. In this paper, a new decomposition-based evolutionary algorithm with adaptive weight adjustment is designed to obtain this goal. The proposed algorithm adopts the uniform design method to set the weight vectors which are uniformly distributed over the design space, and an adaptive weight adjustment is used to solve some MaOPs with complex Pareto optimal front (PF) (i.e., PF with a sharp peak of low tail or discontinuous PF). A selection strategy is used to help solutions to converge to the Pareto optimal solutions. Comparing with some efficient state-of-the-art algorithms, e.g., NSGAII-CE, MOEA/D and HypE, on some benchmark functions, the proposed algorithm is able to find more accurate Pareto front with better diversity.
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This work was supported by National Natural Science Foundations of China (Nos. 61502290, 61401263, 61672334 and 61673251), China Postdoctoral Science Foundation (No. 2015M582606), Fundamental Research Funds for the Central Universities (No. GK201603094 and No. GK201603002) and Natural Science Basic Research Plan in Shaanxi Province of China (Nos. 2016JQ6045 and 2015JQ6228).
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Dai, C., Lei, X. & He, X. A decomposition-based evolutionary algorithm with adaptive weight adjustment for many-objective problems. Soft Comput 24, 10597–10609 (2020). https://doi.org/10.1007/s00500-019-04565-4
- Evolutionary algorithm
- Many-objective optimization
- Adaptive weight adjustment