A competitive mechanism based multi-objective differential evolution algorithm and its application in feature selection

A large number of evolutionary algorithms have been introduced for multi-objective optimization problems in the past two decades. However, the compromise of convergence and diversity of the non-dominated solutions is still the main difficult problem faced by optimization algorithms. To handle this p...

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Bibliographic Details
Published in:Knowledge-based systems Vol. 245; p. 108582
Main Authors: Pan, Jeng-Shyang, Liu, Nengxian, Chu, Shu-Chuan
Format: Journal Article
Language:English
Published: Amsterdam Elsevier B.V 07.06.2022
Elsevier Science Ltd
Subjects:
Algorithms
Business competition
Competition
Competitive mechanism
Convergence
Crowding
Differential evolution
Euclidean geometry
Evolutionary algorithms
Evolutionary computation
Feature selection
multi-objective algorithm
Multiple objective analysis
Mutation
Objectives
Optimization
Optimization algorithms
Strategy
Variants
Differential evolution
Feature selection
Competitive mechanism
multi-objective algorithm
ISSN:0950-7051, 1872-7409
Online Access:Get full text
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Summary:A large number of evolutionary algorithms have been introduced for multi-objective optimization problems in the past two decades. However, the compromise of convergence and diversity of the non-dominated solutions is still the main difficult problem faced by optimization algorithms. To handle this problem, an efficient competitive mechanism based multi-objective differential evolution algorithm (CMODE) is designed in this work. In CMODE, the rank based on the non-dominated sorting and crowding distance is first adopted to create the leader set, which is utilized to lead the evolution of the differential evolution (DE) algorithm. Then, a competitive mechanism using the shift-based density estimation (SDE) strategy is employed to design a new mutation operation for producing offspring, where the SDE strategy is beneficial to balance convergence and diversity. Meanwhile, two variants of the CMODE using the angle competitive mechanism and the Euclidean distance competitive mechanism are proposed. The experimental results on three test suites show that the proposed CMODE performs better than six state-of-the-art multi-objective optimization algorithms on most of the twenty benchmark functions in terms of hypervolume and inverted generation distance. Furthermore, the proposed CMODE is applied to the feature selection problem. The comparison results on feature selection also demonstrate the efficiency of our proposed CMODE. •An efficient competitive mechanism based multi-objective differential evolution algorithm (CMODE) is proposed.•Two variants of the CMODE using the angle competitive mechanism and Euclidean distance competitive mechanism are proposed.•The performance of our proposed CMODE is comprehensively assessed by comparing with six popular MOEAs on twenty benchmarks.•The proposed CMODE is applied to the feature selection problem.
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ISSN:0950-7051
1872-7409
DOI:10.1016/j.knosys.2022.108582