Hybrid of memory and prediction strategies for dynamic multiobjective optimization

Liang, Zhengping; Zheng, Shunxiang; Zhu, Zexuan; Yang, Shengxiang

doi:10.1016/j.ins.2019.01.066

Cited by 100 publications

(22 citation statements)

References 44 publications

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“…In PMS, the stored individuals can be used more efficiently since they are reevaluated before used. Liang et al [18] proposed a hybrid of memory and prediction strategies (HMPS), which devised a memory-based technique to predict the new locations of the individuals.…”

Section: Memory-based Strategiesmentioning

confidence: 99%

“…Rong et al [40] adopted multiple prediction models to relocate the Paretooptimal solutions once the environment changes. Liang et al [18] proposed an MOEA/D-HMPS, which adopted two prediction strategies to relocate the Pareto-optimal solutions according to whether the current environmental change is similar to the historical changes. Rong et al [29] proposed a multi-model prediction (MMP) method to tackle continuous DMOPs with more than one type of the unknown PS changes.…”

Section: Prediction-based Strategiesmentioning

confidence: 99%

“…In recent years, plenty of dynamic handling strategies have been inserted into the classic MOEAs to solve DMOPs. According to their inherent behaviors, the existing techniques can be classified into different categories, such as diversity introduction/maintenance strategies [12][13][14][15], memory-based strategies [16][17][18][19][20], Multipopulation-based strategies [21][22][23][24][25], and prediction-based strategies [18,[26][27][28][29]. Among all of these techniques, multipopulation-based and prediction-based strategies have received great concern.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

A multipopulation evolutionary framework with Steffensen’s method for dynamic multiobjective optimization problems

Cao

Wang

2021

Memetic Comp.

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Dynamic multiobjective optimization problems (DMOPs) require the evolutionary algorithms that can track the moving Pareto-optimal fronts efficiently. This paper presents a dynamic multiobjective evolutionary framework (DMOEF-MS), which adopts a novel multipopulation structure and Steffensen’s method to solve DMOPs. In DMOEF-MS, only one population deals with the original DMOP, while the others focus on single-objective problems that are generated by the weighted summation of the original DMOP. Then, Steffensen’s method is used to control the evolving process in two ways: prediction and diversity-maintenance. Particularly, the prediction strategy is devised to predict the next promising positions for the individuals that handle single-objective problems, and the diversity-maintenance strategy is used to increase population diversity before the environment changes and reinitialize the multiple populations after the environment changes. This paper gives a comprehensive comparison of DMOEF-MS with some state-of-the-art DMOEAs on 14 DMOPs and the experimental results demonstrate the effectiveness of the proposed algorithm.

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Section: Memory-based Strategiesmentioning

confidence: 99%

Section: Prediction-based Strategiesmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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A multipopulation evolutionary framework with Steffensen’s method for dynamic multiobjective optimization problems

Cao

Wang

2021

Memetic Comp.

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“…In most of DMO literature, the main change response can be classified into the following four categories: memory approaches [29], prediction approaches [8], [30], [31], diversity approaches [32], [33], multipopulation approaches [20], [28] and hybrid strategy [34]. These approaches can handle the existing DMOPs better than static MOEAs and for the future development of the DMO have made tremendous research contributions and reference value.…”

Section: Of Convergence and Diversity With Highmentioning

confidence: 99%

“…Many DMOEAs have been proposed in recent years for dealing with DMOPs well, and most existing algorithms can be classified into the different categories (i.e., memory approaches [37], [38], prediction approaches [7], [9], [22], [31], diversity approaches [32], [33], multipopulation approaches [20], [28] and hybrid strategy [34]), not restricted to evolutionary DMO. This section mainly focuses on the prediction approaches.…”

Section: B Related Workmentioning

confidence: 99%

A Novel Prediction Strategy Based on Change Degree of Decision Variables for Dynamic Multi-Objective Optimization

Xing

Liu

et al. 2020

IEEE Access

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Effectively balancing the convergence and diversity in dynamic environments is a challenging task. In order to handle the issue, this paper proposes a novel prediction strategy based on change degree of decision variables for dynamic multi-objective optimization (CDDV), which has the ability to detect the change degree in the decision space and design the different prediction strategy to make the population adapt to the new environment. The proposed method can adaptively increase population diversity according to the analysis of change degree, when an environmental change is detected. In order to study the efficacy and usefulness of the novel change degree on evolutionary algorithms, a range of dynamic multi-objective benchmark problems are selected to evaluate the performance of the proposed algorithm. The results demonstrate the effectiveness of proposed algorithm in compared with four other state-of-the-art methods. INDEX TERMS Dynamic multi-objective optimization, evolutionary algorithms, change degree, diversity.

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A New Prediction-Based Algorithm for Dynamic Multi-objective Optimization Problems

Karkazan¹,

Topcuoglu²,

Sahmoud³

2023

Lecture Notes in Computer Science

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Hybrid of memory and prediction strategies for dynamic multiobjective optimization

Cited by 100 publications

References 44 publications

A multipopulation evolutionary framework with Steffensen’s method for dynamic multiobjective optimization problems

A multipopulation evolutionary framework with Steffensen’s method for dynamic multiobjective optimization problems

A Novel Prediction Strategy Based on Change Degree of Decision Variables for Dynamic Multi-Objective Optimization

A New Prediction-Based Algorithm for Dynamic Multi-objective Optimization Problems

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