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An indicator and adaptive region division based evolutionary algorithm for many-objective optimization
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DOI：10.1016/j.asoc.2020.106872.png)
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Problems involving four or more objectives are termed Many-objective problems (MaOPs), which pose serious challenges to existing evolutionary algorithms (EAs). Although EAs via decomposition exhibit encouraging performance in handling MaOPs, they need a set of predefined weight vectors, which is not well adaptable to problems possessing various Pareto front (PF) shapes. Besides, the nature of subproblem formulations renders the overwhelming convergence property. In this paper, we introduce an indicator and adaptive region division based EA, referred to as IREA, which is free from the presetting of weight vectors. To be specific, an angular distance based space division module and a proximity-oriented indicator are incorporated into IREA, where the former highlights diversity adaptively via maximum angular distance while the latter emphasizes convergence in a local manner. Furthermore, the quality of mating pool is leveraged by a coordinate transformation assisted niche technique. The proposed IREA is compared with several prevalent many-objective EAs on scalable MaOPs with varying characteristics, as well as on the many-objective cloud manufacturing service composition problems. The experimental results demonstrate that IREA is highly competitive and can be used as an alternative for handling MaOPs. (C) 2020 Elsevier B.V. All rights reserved.
Keywords:
Many-objective optimization
Indicator
objective space division
Cloud service composition
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