A dynamic multi-objective evolutionary algorithm based on intensity of environmental change

Hu, Yaru; Zheng, Jinhua; Zou, Juan; Yang, Shengxiang; Ou, Junwei; Wang, Rui

doi:10.1016/j.ins.2020.02.071

Cited by 38 publications

(19 citation statements)

References 33 publications

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“…In this section, we mainly discuss the impact of different parts of the algo- In addition, in order to verify the validity of DVA under the same benchmarks, we compared DVA with a dynamic multi-objective evolutionary algorithm based on intensity of environmental change(IEC) [52]. In IEC, the algorithm effectively tracks the POF according to the intensity of environmental change, and in this paper, JY is also used as the standard test problem set.The specific experimental results are shown in Table 5, and we can intuitively see that in JY1,JY5,JY6,JY7 and JY8, the performance of DVA is significantly better than IEC.…”

Section: Discussionmentioning

confidence: 99%

A prediction strategy based on decision variable analysis for dynamic Multi-objective Optimization

Zheng

Zhou

Zou

et al. 2021

Swarm and Evolutionary Computation

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Section: Discussionmentioning

confidence: 99%

A prediction strategy based on decision variable analysis for dynamic Multi-objective Optimization

Zheng

Zhou

Zou

et al. 2021

Swarm and Evolutionary Computation

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“…This predictable dynamism can be best handled by models [18], [31], [32] that predict the new POF and/or POS upon an environmental change. This has inspired the development of various prediction-based strategies [33], [34], [35]. However, existing prediction-based strategies have some open issues, such as poor performance in the early stages of search and strict underlying assumptions.…”

Section: Related Workmentioning

confidence: 99%

Handling Dynamic Multiobjective Optimization Environments via Layered Prediction and Subspace-Based Diversity Maintenance

Zheng

Jiang

et al. 2023

IEEE Trans. Cybern.

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In this paper, we propose an evolutionary algorithm based on layered prediction (LP) and subspace-based diversity maintenance (SDM) for handling dynamic multi-objective optimization environments. The LP strategy takes into account different levels of progress by different individuals in evolution and historical information to predict the population in the event of environmental changes for a prompt change response. The SDM strategy identifies gaps in population distribution and employs a gap filling technique to increase population diversity. SDM further guides rational population reproduction with a subspace-based probability model to maintain the balance between population diversity and convergence in every generation of evolution regardless of environmental changes. The proposed algorithm has been extensively studied through comparison with five state-of-the-art algorithms on a variety of test problems, demonstrating its effectiveness in dealing with dynamic multiobjective optimization problems.

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“…If none of the termination criteria are met, production new generation will begin. This cycle is repeated until one of the termination criteria is met [64,65].…”

Section: Related Workmentioning

confidence: 99%

A novel community detection based genetic algorithm for feature selection

2021

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The feature selection is an essential data preprocessing stage in data mining. The core principle of feature selection seems to be to pick a subset of possible features by excluding features with almost no predictive information as well as highly associated redundant features. In the past several years, a variety of meta-heuristic methods were introduced to eliminate redundant and irrelevant features as much as possible from high-dimensional datasets. Among the main disadvantages of present meta-heuristic based approaches is that they are often neglecting the correlation between a set of selected features. In this article, for the purpose of feature selection, the authors propose a genetic algorithm based on community detection, which functions in three steps. The feature similarities are calculated in the first step. The features are classified by community detection algorithms into clusters throughout the second step. In the third step, features are picked by a genetic algorithm with a new community-based repair operation. Nine benchmark classification problems were analyzed in terms of the performance of the presented approach. Also, the authors have compared the efficiency of the proposed approach with the findings from four available algorithms for feature selection. Comparing the performance of the proposed method with three new feature selection methods based on PSO, ACO, and ABC algorithms on three classifiers showed that the accuracy of the proposed method is on average 0.52% higher than the PSO, 1.20% higher than ACO, and 1.57 higher than the ABC algorithm.

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A dynamic multi-objective evolutionary algorithm based on intensity of environmental change

Cited by 38 publications

References 33 publications

A prediction strategy based on decision variable analysis for dynamic Multi-objective Optimization

A prediction strategy based on decision variable analysis for dynamic Multi-objective Optimization

Handling Dynamic Multiobjective Optimization Environments via Layered Prediction and Subspace-Based Diversity Maintenance

A novel community detection based genetic algorithm for feature selection

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