A Novel Dynamic Multiobjective Optimization Algorithm With Non-Inductive Transfer Learning Based on Multi-Strategy Adaptive Selection

In this article, a novel multi-strategy adaptive selection-based dynamic multiobjective optimization algorithm (MSAS-DMOA) is proposed, which adopts the non-inductive transfer learning (TL) paradigm to solve dynamic multiobjective optimization problems (DMOPs). In particular, based on a scoring syst...

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Bibliographic Details
Published in:IEEE transaction on neural networks and learning systems Vol. 35; no. 11; pp. 16533 - 16547
Main Authors: Li, Han, Wang, Zidong, Lan, Chengbo, Wu, Peishu, Zeng, Nianyin
Format: Journal Article
Language:English
Published: United States IEEE 01.11.2024
Subjects:
Behavioral sciences
Convergence
Dynamic multiobjective optimization algorithm (DMOA)
evolutionary transfer optimization (ETO)
Heuristic algorithms
kernel mean matching (KMM)
Optimization
Prediction algorithms
Task analysis
Transfer learning
transfer learning (TL)
ISSN:2162-237X, 2162-2388, 2162-2388
Online Access:Get full text
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Summary:In this article, a novel multi-strategy adaptive selection-based dynamic multiobjective optimization algorithm (MSAS-DMOA) is proposed, which adopts the non-inductive transfer learning (TL) paradigm to solve dynamic multiobjective optimization problems (DMOPs). In particular, based on a scoring system that evaluates environmental changes, the source domain is adaptively constructed with several optional groups to enrich the knowledge. Along with a group of guide solutions, the importance of historical experiences is estimated via the kernel mean matching (KMM) method, which avoids designing strategies to label individuals. The proposed MSAS-DMOA is comprehensively evaluated on 14 DMOPs, and the results show an overwhelming performance improvement in terms of both convergence and diversity as compared with other four popular DMOAs. In addition, ablation studies are also conducted to validate the superiority of the applied strategies in MSAS-DMOA, which can effectively alleviate the negative transfer phenomenon. Without the conventional labeling procedure, the proposed method also yields satisfactory results, which can provide valuable reference for designing other evolutionary transfer optimization (ETO) algorithms.
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ISSN:2162-237X
2162-2388
2162-2388
DOI:10.1109/TNNLS.2023.3295461