Operational decomposition for large scale multi-objective optimization problems

Antonio, Luis; Coello, Carlos A. Coello; Brambila, Silvia González; González, Josué Figueroa; Tapia, Guadalupe Castillo

doi:10.1145/3319619.3322068

Cited by 20 publications

(1 citation statement)

References 7 publications

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“…In contrast, the diversity-related variables help LSMOEAs find the solution sets with a better distribution. Existing decision-variable grouping strategies can be divided into fixed grouping strategies [ 13 , 14 , 15 , 16 , 17 , 18 , 19 , 20 ] and dynamic grouping strategies [ 21 , 22 , 23 , 24 ]. In a fixed grouping strategy, the grouping results do not change during the evolution process, i.e., the evolutionary algorithm for large-scale many-objective optimization, LMEA.…”

Section: Introductionmentioning

confidence: 99%

A Stable Large-Scale Multiobjective Optimization Algorithm with Two Alternative Optimization Methods

Liu

Zhu

Cao

2023

Entropy

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For large-scale multiobjective evolutionary algorithms based on the grouping of decision variables, the challenge is to design a stable grouping strategy to balance convergence and population diversity. This paper proposes a large-scale multiobjective optimization algorithm with two alternative optimization methods (LSMOEA-TM). In LSMOEA-TM, two alternative optimization methods, which adopt two grouping strategies to divide decision variables, are introduced to efficiently solve large-scale multiobjective optimization problems. Furthermore, this paper introduces a Bayesian-based parameter-adjusting strategy to reduce computational costs by optimizing the parameters in the proposed two alternative optimization methods. The proposed LSMOEA-TM and four efficient large-scale multiobjective evolutionary algorithms have been tested on a set of benchmark large-scale multiobjective problems, and the statistical results demonstrate the effectiveness of the proposed algorithm.

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