Promoting Objective Knowledge Transfer: A Cascaded Fuzzy System for Solving Dynamic Multiobjective Optimization Problems

In this article, a novel dynamic multiobjective optimization algorithm (DMOA) with a cascaded fuzzy system (CFS) is developed, which aims to promote objective knowledge transfer from an innovative perspective of comprehensive information characterization. This development seeks to overcome the bottl...

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Bibliographic Details
Published in:IEEE transactions on fuzzy systems Vol. 32; no. 11; pp. 6199 - 6213
Main Authors: Li, Han, Wang, Zidong, Zeng, Nianyin, Wu, Peishu, Li, Yurong
Format: Journal Article
Language:English
Published: IEEE 01.11.2024
Subjects:
cascaded fuzzy system
Dynamic multiobjective optimization algorithm (DMOA)
evolutionary transfer optimization (ETO)
Fuzzy systems
Heuristic algorithms
information characterization
Knowledge transfer
negative transfer
Optimization
Prediction algorithms
Social factors
Statistics
ISSN:1063-6706, 1941-0034
Online Access:Get full text
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Summary:In this article, a novel dynamic multiobjective optimization algorithm (DMOA) with a cascaded fuzzy system (CFS) is developed, which aims to promote objective knowledge transfer from an innovative perspective of comprehensive information characterization. This development seeks to overcome the bottleneck of negative transfer in evolutionary transfer optimization (ETO)-based algorithms. Specifically, previous Pareto solutions, center- and knee-points of multisubpopulation are adaptively selected to establish the source domain, which are then assigned soft labels through the designed CFS, based on a thorough evaluation of both convergence and diversity. A target domain is constructed by centroid feed-forward of multisubpopulation, enabling further estimations on learning samples with the assistance of the kernel mean matching (KMM) method. By doing so, the property of nonindependently identically distributed data is considered to enhance efficient knowledge transfer. Extensive evaluation results demonstrate the reliability and superiority of the proposed CFS-DMOA in solving dynamic multiobjective optimization problems, showing significant competitiveness in terms of mitigating negative transfer as compared to other state-of-the-art ETO-based DMOAs. Moreover, the effectiveness of the soft labels provided by CFS in breaking the "either/or" limitation of hard labels is validated, facilitating a more flexible and comprehensive characterization of historical information, thereby promoting objective and effective knowledge transfer.
ISSN:1063-6706
1941-0034
DOI:10.1109/TFUZZ.2024.3443207