Journal
APPLIED SOFT COMPUTING
Volume 84, Issue -, Pages -Publisher
ELSEVIER
DOI: 10.1016/j.asoc.2019.105731
Keywords
Many-objective optimization; Evolutionary algorithm; Objective space decomposition; Adaptive weight generation
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Funding
- National Natural Science Foundation of China [61773410]
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Decomposition is a representative method for handling many-objective optimization problems with evolutionary algorithms. Classical decomposition scheme relies on a set of uniformly distributed reference vectors to divide the objective space into multiple subregions. This scheme often works poorly when the problem has an irregular Pareto front due to the inconsistency between the distribution of reference vectors and the shape of Pareto fronts. We propose in this paper an adaptive weighted decomposition based many-objective evolutionary algorithm to tackle complicated many-objective problems whose Pareto fronts may or may not be regular. Unlike traditional decomposition based algorithms that use a pre-defined set of reference vectors, the reference vectors in the proposed algorithm are produced from the population during the search. The experiments show that the performance of the proposed algorithm is competitive with other state-of-the-art algorithms and is less-sensitive to the irregularity of the Pareto fronts. (C) 2019 Elsevier B.V. All rights reserved.
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