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A many-objective evolutionary algorithm based on corner solution and cosine distance
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AI
DOI：10.1007/s10489-022-03883-9.png)
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Abstract
En 中文
Most many-objective optimization algorithms focus on balancing convergence and diversity, instead of attaching importance to the contribution of the boundary solution. The boundary solution is beneficial for enhancing the PF coverage; therefore, we propose a many-objective evolutionary algorithm based on the corner solution and cosine distance (MaOEA-CSCD) to balance convergence and diversity, as well as protect the PF boundary. We set a corner solution archive to store the corner solutions and apply these corner solutions and cosine distance in the mating strategy to improve the quality of the parents to generate high-quality offspring. In environmental selection, a greedy strategy is applied to select the corner solution and the solution with better convergence to overcome the insufficient selection pressure while protecting the PF boundary and guaranteeing the search space. Then, a selection-deletion strategy is used to balance convergence and diversity, it first selects solutions based on the maximum cosine distance, and then considers replacement solutions based on convergence. The comparison of MaOEA-CSCD with six algorithms on 25 benchmark and three real-world optimization problems shows that it is competitive.
Keywords:
Many-objective optimization
Evolutionary algorithm
Eonvergence and diversity
Corner solution