An Inhomogeneous Grid-Based Evolutionary Algorithm for Many-Objective Optimization

He, Maowei; Xia, Haitao; Chen, Hanning; Ma, Lianbo

doi:10.1109/access.2022.3176372

Cited by 1 publication

(1 citation statement)

References 49 publications

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“…11 According to selection strategies of MaOEAs, existing research work can be divided into four categories: the dominance-based selection method, 12 the decomposition-based selection method, 13 the indicator-based selection method, 14 and the archive-based selection method. 15 Traditional dominance-based selection strategy 16 uses Pareto dominance method to select elite solutions, and updates the non-dominant solutions according to the diversity. However, the traditional selection strategies based on Pareto dominance method are limited.…”

Section: Introductionmentioning

confidence: 99%

A many‐objective optimization algorithm with dual criteria and mixed distribution correction strategy

Zhang

Cai

et al. 2023

Concurrency and Computation

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SummaryIn many‐objective optimization algorithms, it is very important to maintain significant convergence and diversity of the population. And with the increasing demand in various fields, the optimization problem also becomes gradually complicated. Some existing many‐objective optimization algorithms are faced with challenges such as domination resistance and dimensional crisis. To solve these challenges, a many‐objective optimization algorithm based on dual criteria and mixed distribution correction strategy (MaOEA‐CSMDC) is proposed in this paper. To be specific, a matching selection strategy based on dual criteria combined by pareto domination strategy and achievement scalar function, which alleviates the domination resistance phenomenon and enhances the selection pressure of the algorithm. After that, an environment selection strategy based on equal probability mixed distribution correction is designed to better balance convergence and diversity. In this strategy, normal distribution, exponential distribution, and Cauchy distribution are introduced to adjust the weight of convergence and diversity in evolution by means of equal probability, so as to alleviate the problem that the conflict between them is intensified in the later stage of the algorithm. The experimental results show that, MaOEA‐CSMDC not only has advantages in convergence and diversity indicators, but also is more competitive in solving many‐objective optimization problems.

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