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MMOEA-SP: A multistage many-objective evolutionary algorithm based on sampling points
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AI
DOI：10.1016/j.knosys.2022.108677.png)
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Abstract
En 中文
For the optimization algorithm based on Pareto dominance, how to obtain excellent performance in the high-dimensional objective space is a crucial issue. If the extreme solutions are correctly identified, the algorithm process can be accelerated. However, the existence of the dominance resistant solutions makes it suffer a devastating blow, especially when solving many-objective optimization problems. To address this issue, a multistage many-objective evolutionary algorithm based on sampling points (MMOEA-SP) is proposed. First of all, a state judgment strategy based on sampling points is developed to guide the population to explore or exploit, which divides the population into two stages and assigns different matching pools. The purpose of this strategy is to balance the convergence and diversity of the population. In the environment selection mechanism, two strategies are considered. On the one hand, a cluster-based dominance resistant solutions elimination strategy is introduced to accelerate population convergence and narrow the search area. On the other hand, solutions with larger differences are given priority as candidate solutions in the environmental selection mechanism to increase the diversity. The proposed algorithm is compared with plentiful state-of-the-art algorithms on a number of well-known benchmarks with 5-20 objectives. Finally, the experimental results show that the proposed algorithm can perform well on most of the test functions and generally outperforms its competitors. (c) 2022 Elsevier B.V. All rights reserved.
Keywords:
Evolutionary algorithm
Many-objective optimization
Multistage
Dominance resistant solutions
Convergence
Sampling points