A Novel Dynamic Multiobjective Optimization Algorithm With Hierarchical Response System

In this article, a novel dynamic multiobjective optimization algorithm (DMOA) is proposed based on a designed hierarchical response system (HRS). Named HRS-DMOA, the proposed algorithm mainly aims at integrating merits from the mainstream ideas of dynamic behavior handling (i.e., the diversity-, mem...

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Bibliographic Details
Published in:IEEE transactions on computational social systems Vol. 11; no. 2; pp. 2494 - 2512
Main Authors: Li, Han, Wang, Zidong, Lan, Chengbo, Wu, Peishu, Zeng, Nianyin
Format: Journal Article
Language:English
Published: Piscataway IEEE 01.04.2024
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:
Ablation
Algorithms
Behavioral sciences
Convergence
Dynamic multiobjective optimization algorithm (DMOA)
evolutionary algorithm
Heuristic algorithms
hierarchical response system (HRS)
Multiple objective analysis
Optimization
Optimization algorithms
Pareto optimization
Prediction algorithms
Social factors
Statistics
transfer learning (TL)
ISSN:2329-924X, 2373-7476
Online Access:Get full text
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Summary:In this article, a novel dynamic multiobjective optimization algorithm (DMOA) is proposed based on a designed hierarchical response system (HRS). Named HRS-DMOA, the proposed algorithm mainly aims at integrating merits from the mainstream ideas of dynamic behavior handling (i.e., the diversity-, memory-, and prediction-based methods) in order to make flexible responses to environmental changes. In particular, by two predefined thresholds, the environmental changes are quantified as three levels. In case of a slight environmental change, the previous Pareto set-based refinement strategy is recommended, while the diversity-based reinitialization method is applied in case of a dramatic environmental change. For changes occurring at a medium level, the transfer-learning-based response is adopted to make full use of the historical searching experiences. The proposed HRS-DMOA is comprehensively evaluated on a series of benchmark functions, and the results show an improved comprehensive performance as compared with four popular baseline DMOAs in terms of both convergence and diversity, which also outperforms other two state-of-the-art DMOAs in ten out of 14 testing cases, exhibiting the competitiveness and superiority of the algorithm. Finally, extensive ablation studies are carried out, and from the results, it is found that as compared with randomly selecting the response methods, the proposed HRS enables more reasonable and efficient responses in most cases. In addition, the generalization ability of the proposed HRS as a flexible plug-and-play module to handle dynamic behaviors is proven as well.
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ISSN:2329-924X
2373-7476
DOI:10.1109/TCSS.2023.3293331