research-article

Improved Reference Vector Guided Differential Evolution Algorithm for Many-Objective Optimization

Authors:

Shaoyong Zheng,

Yunliang LongAuthors Info & Claims

ICMAI '20: Proceedings of the 2020 5th International Conference on Mathematics and Artificial Intelligence

Pages 43 - 49

https://doi.org/10.1145/3395260.3395268

Published: 29 May 2020 Publication History

Abstract

Most of the existing evolutionary algorithms to deal with many-objective problems are based on the enhancing of selection strategy. Among them, the reference vector-guided evolutionary algorithm (RVEA) achieves excellent performance. In this paper, a new search engine is combined with RVEA to achieve further performance enhancement of the differential evolutionary (DE) algorithm. In the optimization process of differential evolution algorithm on many-objective problems, improving convergence and maintaining diversity are two different optimization directions, and it is usually difficult to maintain a balance between them. To solve this problem, a new search engine based on DE is proposed. The proposed search engine is implemented based on a cooperative scheme of local and global search strategies. In the local search, the population is divided into several sub-populations, each of which evolves independently using the proposed mutation strategy. The distance between the individuals in each sub-population is relatively close. Therefore, it has a strong exploitation capability, and will not make the population lose diversity. Meanwhile, the selection strategy of RVEA enables the population to maintain diversity, and the DE/rand/1 utilized in global search is sufficient to keep a strong exploration capability. Therefore, the proposed approach can achieve a good balance between exploration and exploitation. The experimental results show that the proposed algorithm performs well in many-objective optimizations up to more than 10 objectives.

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Cited By

Yang LZhang YCao JLi KWang D(2024)A many-objective evolutionary algorithm based on reference vector guided selection and two diversity and convergence enhancement strategiesApplied Soft Computing10.1016/j.asoc.2024.111369154:COnline publication date: 2-Jul-2024
https://dl.acm.org/doi/10.1016/j.asoc.2024.111369
Yang LZhang YCao J(2024)A Reference Vector Guided Evolutionary Algorithm with Diversity and Convergence Enhancement Strategies for Many-Objective OptimizationIntelligence Computation and Applications10.1007/978-981-97-4393-3_7(73-87)Online publication date: 2-Jul-2024
https://doi.org/10.1007/978-981-97-4393-3_7

Index Terms

Improved Reference Vector Guided Differential Evolution Algorithm for Many-Objective Optimization
1. Computing methodologies
  1. Artificial intelligence
    1. Search methodologies
      1. Heuristic function construction

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ICMAI '20: Proceedings of the 2020 5th International Conference on Mathematics and Artificial Intelligence

April 2020

252 pages

ISBN:9781450377072

DOI:10.1145/3395260

Copyright © 2020 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].

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Southwest Jiaotong University
Xihua University: Xihua University

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Publication History

Published: 29 May 2020

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ICMAI 2020

ICMAI 2020: 2020 5th International Conference on Mathematics and Artificial Intelligence

April 10 - 13, 2020

Chengdu, China

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Cited By

Yang LZhang YCao JLi KWang D(2024)A many-objective evolutionary algorithm based on reference vector guided selection and two diversity and convergence enhancement strategiesApplied Soft Computing10.1016/j.asoc.2024.111369154:COnline publication date: 2-Jul-2024
https://dl.acm.org/doi/10.1016/j.asoc.2024.111369
Yang LZhang YCao J(2024)A Reference Vector Guided Evolutionary Algorithm with Diversity and Convergence Enhancement Strategies for Many-Objective OptimizationIntelligence Computation and Applications10.1007/978-981-97-4393-3_7(73-87)Online publication date: 2-Jul-2024
https://doi.org/10.1007/978-981-97-4393-3_7

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