Hyperplane area selection evolutionary algorithm for multi-objective optimization problems

Kang Wang; Xinye Cai

doi:10.1117/12.2640876

2 December 2022 Hyperplane area selection evolutionary algorithm for multi-objective optimization problems

Kang Wang, Xinye Cai

Proceedings Volume 12288, International Conference on Computer, Artificial Intelligence, and Control Engineering (CAICE 2022); 122881C (2022) https://doi.org/10.1117/12.2640876
Event: International Conference on Computer, Artificial Intelligence, and Control Engineering (CAICE 2022), 2022, Zhuhai, China

Abstract

In the multi-objective optimization problem, it is an arduous task to select sub-populations with good convergence and diversity under the environment selection of evolutionary algorithms. Especially as the number of objectives increases, the number of non-dominated solutions will also increase sharply, so it becomes more challenging to increase the selection pressure on Pareto. To solve these problems, this paper proposes a hyperplane area selection evolutionary algorithm (HASEA). The algorithm constructs hyperplane area by the boundary points of individuals in the population and then projects all individuals to the corresponding area along the direction of the hyperplane orthogonal vector, and finally, each area selects corresponding individuals to form sub-populations through some strategies. Experimental results show that the subpopulations selected by this strategy have both convergence and diversity.

Citation Download Citation

Kang Wang and Xinye Cai "Hyperplane area selection evolutionary algorithm for multi-objective optimization problems", Proc. SPIE 12288, International Conference on Computer, Artificial Intelligence, and Control Engineering (CAICE 2022), 122881C (2 December 2022); https://doi.org/10.1117/12.2640876

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