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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Vydané v:Knowledge-based systems Ročník 245; s. 108582
Hlavní autori: Pan, Jeng-Shyang, Liu, Nengxian, Chu, Shu-Chuan
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: Amsterdam Elsevier B.V 07.06.2022
Elsevier Science Ltd
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ISSN:0950-7051, 1872-7409
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Abstract 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.
AbstractList 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.
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.
ArticleNumber 108582
Author Liu, Nengxian
Chu, Shu-Chuan
Pan, Jeng-Shyang
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  surname: Pan
  fullname: Pan, Jeng-Shyang
  email: jspan@cc.kuas.edu.tw
  organization: College of Computer Science and Engineering, Shandong University of Science and Technology, Qingdao, China
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  givenname: Nengxian
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  surname: Liu
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  organization: College of Mathematics and Computer Science, Fuzhou University, Fuzhou, China
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  givenname: Shu-Chuan
  orcidid: 0000-0003-2117-0618
  surname: Chu
  fullname: Chu, Shu-Chuan
  email: scchu0803@gmail.com
  organization: College of Computer Science and Engineering, Shandong University of Science and Technology, Qingdao, China
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Keywords Differential evolution
Feature selection
Competitive mechanism
multi-objective algorithm
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Snippet A large number of evolutionary algorithms have been introduced for multi-objective optimization problems in the past two decades. However, the compromise of...
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StartPage 108582
SubjectTerms 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
Title A competitive mechanism based multi-objective differential evolution algorithm and its application in feature selection
URI https://dx.doi.org/10.1016/j.knosys.2022.108582
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