An indicator and adaptive region division based evolutionary algorithm for many-objective optimization

Zhou, Jiajun; Yao, Xifan; Gao, Liang; Hu, Chengyu

doi:10.1016/j.asoc.2020.106872

Cited by 18 publications

(1 citation statement)

References 48 publications

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“…Indicator-based algorithms employ performance indicators, such as IGD [23], HV [24], R2 [25,26], and I ε + [27,28] to select solutions during the evolutionary process. The high computational burden hinders the wide application of such approaches, especially for HV in high-dimensional objective space, even though there is Monte Carlo sampling method.…”

Section: Introductionmentioning

confidence: 99%

A many-objective evolutionary algorithm with metric-based reference vector adjustment

Wang

Zhang

Mao

2023

Complex Intell. Syst.

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To deal with the inconsistency of the distribution of reference vectors (RVs) and the shape of Pareto front in decomposition-based multiobjective evolutionary algorithms, many literatures proposed to adjust RVs in evolutionary process. However, most of the existing algorithms adjust RVs either in each generation or at a fixed frequency, not considering the evolving information of the population. To tackle this issue, we propose a many-objective evolutionary algorithm with metric-based reference vector adjustment named MBRA. To adjust RVs periodically and conditionally, we use the improvement rate of convergence degree of subproblems computed through d1 distance to reflect the whole convergence of the population. Only when the subproblems are considered convergent on the whole, are RVs allowed to be adjusted: delete the invalid RVs associated with no solutions and add new ones using the farthest solution in the subregion of the most crowded RVs for diversity. Besides, in mating selection and environmental selection, we adopt the sum of objectives and fitness-based sorting based on $$I_{\varepsilon + }$$ I ε + as the secondary principles to promote convergence, respectively. Validated through extensive experiments, MBRA is effective and competitive on many-objective optimization problems, especially for problems with irregular Pareto fronts.

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