A cluster prediction strategy with the induced mutation for dynamic multi-objective optimization

Xu, Kangyu; Xia, Yizhang; Zou, Juan; Hou, Zhanglu; Yang, Shengxiang; Hu, Yaru; Liu, Yuan

doi:10.1016/j.ins.2024.120193

Information Sciences

2024

DOI: 10.1016/j.ins.2024.120193

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A cluster prediction strategy with the induced mutation for dynamic multi-objective optimization

Kangyu Xu,

Yizhang Xia,

Juan Zou

et al.

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Cited by 5 publications

References 38 publications

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Dynamic multi-objective optimization based on classification response of decision variables

Li,

Liu,

Wang

2025

Information Sciences

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Dynamic multi-objective optimization based on classification response of decision variables

Li,

Liu,

Wang

2025

Information Sciences

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A novel preference-driven evolutionary algorithm for dynamic multi-objective problems

Wang,

Zheng,

Hou

et al. 2024

Swarm and Evolutionary Computation

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Combining kernelised autoencoding and centroid prediction for dynamic multi‐objective optimisation

Hou,

Zou,

Ruan

et al. 2024

CAAI Trans on Intel Tech

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Evolutionary algorithms face significant challenges when dealing with dynamic multi‐objective optimisation because Pareto optimal solutions and/or Pareto optimal fronts change. The authors propose a unified paradigm, which combines the kernelised autoncoding evolutionary search and the centroid‐based prediction (denoted by KAEP), for solving dynamic multi‐objective optimisation problems (DMOPs). Specifically, whenever a change is detected, KAEP reacts effectively to it by generating two subpopulations. The first subpopulation is generated by a simple centroid‐based prediction strategy. For the second initial subpopulation, the kernel autoencoder is derived to predict the moving of the Pareto‐optimal solutions based on the historical elite solutions. In this way, an initial population is predicted by the proposed combination strategies with good convergence and diversity, which can be effective for solving DMOPs. The performance of the proposed method is compared with five state‐of‐the‐art algorithms on a number of complex benchmark problems. Empirical results fully demonstrate the superiority of the proposed method on most test instances.

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A cluster prediction strategy with the induced mutation for dynamic multi-objective optimization

Cited by 5 publications

References 38 publications

Dynamic multi-objective optimization based on classification response of decision variables

Dynamic multi-objective optimization based on classification response of decision variables

A novel preference-driven evolutionary algorithm for dynamic multi-objective problems

Combining kernelised autoencoding and centroid prediction for dynamic multi‐objective optimisation

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