A Survey on Learnable Evolutionary Algorithms for Scalable Multiobjective Optimization

Recent decades have witnessed great advancements in multiobjective evolutionary algorithms (MOEAs) for multiobjective optimization problems (MOPs). However, these progressively improved MOEAs have not necessarily been equipped with scalable and learnable problem-solving strategies for new and grand...

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Bibliographic Details
Published in:IEEE transactions on evolutionary computation Vol. 27; no. 6; pp. 1941 - 1961
Main Authors: Liu, Songbai, Lin, Qiuzhen, Li, Jianqiang, Tan, Kay Chen
Format: Journal Article
Language:English
Published: New York IEEE 01.12.2023
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:
Evolution (biology)
Evolutionary algorithms
Evolutionary computation
Generators
Genetic algorithms
Learnable evolutionary algorithms
Machine learning
machine learning (ML)
Multiple objective analysis
Optimization
scalable multiobjective optimization
Scaling
Search problems
Sociology
Statistics
Taxonomy
ISSN:1089-778X, 1941-0026
Online Access:Get full text
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Summary:Recent decades have witnessed great advancements in multiobjective evolutionary algorithms (MOEAs) for multiobjective optimization problems (MOPs). However, these progressively improved MOEAs have not necessarily been equipped with scalable and learnable problem-solving strategies for new and grand challenges brought by the scaling-up MOPs with continuously increasing complexity from diverse aspects, mainly, including expensive cost of function evaluations, many objectives, large-scale search space, time-varying environments, and multitask. Under different scenarios, divergent thinking is required in designing new powerful MOEAs for solving them effectively. In this context, research studies on learnable MOEAs with machine learning techniques have received extensive attention in the field of evolutionary computation. This article begins with a general taxonomy of scaling-up MOPs and learnable MOEAs, followed by an analysis of the challenges that these MOPs pose to traditional MOEAs. Then, we synthetically overview recent advances of learnable MOEAs in solving various scaling-up MOPs, focusing primarily on four attractive directions (i.e., learnable evolutionary discriminators for environmental selection, learnable evolutionary generators for reproduction, learnable evolutionary evaluators for function evaluations, and learnable evolutionary transfer modules for sharing or reusing optimization experience). The insight of learnable MOEAs is offered to readers as a reference to the general track of the efforts in this field.
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ISSN:1089-778X
1941-0026
DOI:10.1109/TEVC.2023.3250350