Multiregional co-evolutionary algorithm for dynamic multiobjective optimization

Ma, Xianyong; Yang, Jingming; Sun, Hao; Hu, Ziyu; Wei, Lixin

doi:10.1016/j.ins.2020.07.009

Cited by 56 publications

(16 citation statements)

References 36 publications

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“…Recently, Ma et al [105] have proposed to introduce random solutions in diferent regions of the objective space, which ensures that introduced solutions to the population are well distributed. Orouskhani et al [118] increased population diversity in the event of changes by a Borda selection approach.…”

Section: Diversity Increase/maintenancementioning

confidence: 99%

“…When environmental changes that make consecutive PSs dissimilar, it is inefective to use a single PS centre to generate a population as an approximation to the new PS. This limitation can be addressed by multi-directional prediction strategies [86,105,128]. In [86], the PS is approximated by multiple sub-populations with the aid of reference points, and each sub-population in new environments is reinitialised based on a centre, predicted by a linear model of Eq.…”

Section: Prediction Based Approachesmentioning

confidence: 99%

“…Similarly, Rong et al [128] proposed a multi-directional prediction approach to predict multiple points on the PS. This approach has also been employed in a recent work [105].…”

Section: Prediction Based Approachesmentioning

confidence: 99%

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Evolutionary Dynamic Multi-objective Optimisation: A Survey

et al. 2022

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Evolutionary dynamic multi-objective optimisation (EDMO) is a relatively young but rapidly growing area of investigation. EDMO employs evolutionary approaches to handle multi-objective optimisation problems that have time-varying changes in objective functions, constraints and/or environmental parameters. Due to the simultaneous presence of dynamics and multi-objectivity in problems, the optimisation difficulty for EDMO has a marked increase compared to that for single-objective or stationary optimisation. After nearly two decades of community effort, EDMO has achieved significant advancements on various topics, including theoretic research and applications. This paper presents a broad survey and taxonomy of existing research on EDMO. Multiple research opportunities are highlighted to further promote the development of the EDMO research field.

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Section: Diversity Increase/maintenancementioning

confidence: 99%

Section: Prediction Based Approachesmentioning

confidence: 99%

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Evolutionary Dynamic Multi-objective Optimisation: A Survey

et al. 2022

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“…In recent years, there are much literature on DMOPs. These literature show that it is widely used in engineering and research on DMOPs in various application areas [1][2][3][4][5][6][7][8][9][10]. Meanwhile, some other scientific problems, such as intelligent learning [11], bilevel optimization [12], can be solved by means of DMOPs.…”

Section: Introductionmentioning

confidence: 99%

An environmental selection and transfer learning-based dynamic multiobjective optimization evolutionary algorithm

Xiang

Ren

2022

Nonlinear Dyn

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In recent years, the dynamic multiobjective optimization problems (DMOPs), whose major strategy is to track the varying PS (Pareto Optimal Solution, PS) and/or PF (Pareto Optimal Frontier), caused a great deal of attention worldwide. As a promising solution, reusing of "experiences" to establish a prediction model is proved to be very useful and widely used in practice. However, most existing methods overlook the importance of environmental selection in the evolutionary processes. In this paper, we propose a dynamic multiobjective optimal evolutionary algorithm which is based on environmental selection and transfer learning (DMOEA-ESTL). This approach makes full use of the environmental selection and transfer learning technique to generate individuals for a new environment by reusing experience to maintain the diversity of the population and speed up the evolutionary process. As experimental validation, we embed this new scheme in the NSGA-II (non-dominated sorting genetic algorithm). We test the proposed algorithm with the help of six benchmark functions as well as compare it with the other two prediction based strategies FPS (Forward-looking Prediction Strategy, FPS) and PPS (Population Prediction Strategy, PPS). The experimental results testify that the proposed strategy can deal with the DMOPs effectively.

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Section: Introductionmentioning

confidence: 99%

An Environmental Selection and Transfer Learning Based Dynamic Multiobjective Optimization Evolutionary Algorithm

He¹,

Xiang²,

Ren³

2021

Preprint

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In recent years, the dynamic multiobjective optimization problems (DMOPs), whose major strategy is to track the varying PS (Pareto Optimal Solution, PS) and/or PF (Pareto Optimal Frontier), caused a great deal of attention worldwide. As a promising solution, reusing of “experiences” to establish a prediction model is proved to be very useful and widely used in practice. However, most existing methods overlook the importance of environmental selection in the evolutionary processes. In this paper, we propose a dynamic multiobjective optimal evolutionary algorithm which is based on environmental selection and transfer learning (DMOEA-ESTL). This approach makes full use of the environmental selection and transfer learning technique to generate individuals for a new environment by reusing experience to maintain the diversity of the population and speed up the evolutionary process. As experimental validation, we embed this new scheme in the NSGA-II (non-dominated sorting genetic algorithm). We test the proposed algorithm with the help of six benchmark functions as well as compare it with the other two prediction based strategies FPS (Forward-looking Prediction Strategy, FPS) and PPS (Population Prediction Strategy, PPS). The experimental results testify that the proposed strategy can deal with the DMOPs effectively.

show abstract

Multiregional co-evolutionary algorithm for dynamic multiobjective optimization

Cited by 56 publications

References 36 publications

Evolutionary Dynamic Multi-objective Optimisation: A Survey

Evolutionary Dynamic Multi-objective Optimisation: A Survey

An environmental selection and transfer learning-based dynamic multiobjective optimization evolutionary algorithm

An Environmental Selection and Transfer Learning Based Dynamic Multiobjective Optimization Evolutionary Algorithm

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