Multiobjective differential evolution enhanced with principle component analysis for constrained optimization

Huang, W., Xu, T., Li, K. and He, J. ORCID: 0000-0002-5616-4691, 2019. Multiobjective differential evolution enhanced with principle component analysis for constrained optimization. Swarm and Evolutionary Computation, 50: 100571. ISSN 2210-6502

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Abstract

Multiobjective evolutionary algorithms (MOEAs) have been successfully applied to a number of constrained optimization problems. Many of them adopt mutation and crossover operators from differential evolution. However, these operators do not explicitly utilise features of fitness landscapes. To improve the performance of algorithms, this paper aims at designing a search operator adapting to fitness landscapes. Through an observation, we find that principle component analysis (PCA) can be used to characterise fitness landscapes. Based on this finding, a new search operator, called PCA-projection, is proposed. In order to verify the effectiveness of PCA-projection, we design two algorithms enhanced with PCA-projection for solving constrained optimization problems, called PMODE and HECO-PDE, respectively. Experiments have been conducted on the IEEE CEC 2017 competition benchmark suite in constrained optimization. PMODE and HECO-PDE are compared with the algorithms from the IEEE CEC 2018 competition and another recent MOEA for constrained optimization. Experimental results show that an algorithm enhanced with PCA-projection performs better than its corresponding opponent without this operator. Furthermore, HECO-PDE is ranked first on all dimensions according to the competition rules. This study reveals that decomposition-based MOEAs, such as HECO-PDE, are competitive with best single-objective and multiobjective evolutionary algorithms for constrained optimization, but MOEAs based on non-dominance, such as PMODE, may not.

Item Type: Journal article
Publication Title: Swarm and Evolutionary Computation
Creators: Huang, W., Xu, T., Li, K. and He, J.
Publisher: Elsevier
Date: November 2019
Volume: 50
ISSN: 2210-6502
Identifiers:
NumberType
10.1016/j.swevo.2019.100571DOI
S2210650218301846Publisher Item Identifier
Divisions: Schools > School of Science and Technology
Depositing User: Jonathan Gallacher
Date Added: 12 Sep 2019 13:26
Last Modified: 04 Oct 2019 07:51
URI: http://irep.ntu.ac.uk/id/eprint/37647

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