A double association-based evolutionary algorithm for many-objective optimization
In this paper, a double association-based evolutionary algorithm (denoted as DAEA) is proposed to solve many-objective optimization problems. In the proposed DAEA, a double association strategy is designed to associate solutions with each subspace. Different from the existing association methods, th...
| Main Authors: | , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
AIMS Press
2023-09-01
|
| Series: | Mathematical Biosciences and Engineering |
| Subjects: | |
| Online Access: | https://www.aimspress.com/article/doi/10.3934/mbe.2023771?viewType=HTML |
| _version_ | 1797663719342211072 |
|---|---|
| author | Junhua Liu Wei Zhang Mengnan Tian Hong Ji Baobao Liu |
| author_facet | Junhua Liu Wei Zhang Mengnan Tian Hong Ji Baobao Liu |
| author_sort | Junhua Liu |
| collection | DOAJ |
| description | In this paper, a double association-based evolutionary algorithm (denoted as DAEA) is proposed to solve many-objective optimization problems. In the proposed DAEA, a double association strategy is designed to associate solutions with each subspace. Different from the existing association methods, the double association strategy takes the empty subspace into account and associates it with a promising solution, which can facilitate the exploration of unknown areas. Besides, a new quality evaluation scheme is developed to evaluate the quality of each solution in subspace, where the convergence and diversity of each solution is first measured, and in order to evaluate the diversity of solutions more finely, the global diversity and local diversity is designed to measure the diversity of each solution. Then, a dynamic penalty coefficient is designed to balance the convergence and diversity by penalizing the global diversity distribution of solutions. The performance of DAEA is validated by comparing with five state-of-the-art many-objective evolutionary algorithms on a number of well-known benchmark problems with up to 20 objectives. Experimental results show that our DAEA has high competitiveness in solving many-objective optimizatiopn problems compared with the other compared algorithms. |
| first_indexed | 2024-03-11T19:18:49Z |
| format | Article |
| id | doaj.art-187bbeb33b5548088e9010c11fc31162 |
| institution | Directory Open Access Journal |
| issn | 1551-0018 |
| language | English |
| last_indexed | 2024-03-11T19:18:49Z |
| publishDate | 2023-09-01 |
| publisher | AIMS Press |
| record_format | Article |
| series | Mathematical Biosciences and Engineering |
| spelling | doaj.art-187bbeb33b5548088e9010c11fc311622023-10-08T01:25:17ZengAIMS PressMathematical Biosciences and Engineering1551-00182023-09-01209173241735510.3934/mbe.2023771A double association-based evolutionary algorithm for many-objective optimizationJunhua Liu0Wei Zhang1Mengnan Tian2Hong Ji 3Baobao Liu 41. The Shaanxi Key Laboratory of Clothing Intelligence, School of Computer Science, Xi'an Polytechinic University, Xi'an 710048, China2. College of Information Science and Engineering, Northeastern University, Shenyang, China1. The Shaanxi Key Laboratory of Clothing Intelligence, School of Computer Science, Xi'an Polytechinic University, Xi'an 710048, China1. The Shaanxi Key Laboratory of Clothing Intelligence, School of Computer Science, Xi'an Polytechinic University, Xi'an 710048, China1. The Shaanxi Key Laboratory of Clothing Intelligence, School of Computer Science, Xi'an Polytechinic University, Xi'an 710048, ChinaIn this paper, a double association-based evolutionary algorithm (denoted as DAEA) is proposed to solve many-objective optimization problems. In the proposed DAEA, a double association strategy is designed to associate solutions with each subspace. Different from the existing association methods, the double association strategy takes the empty subspace into account and associates it with a promising solution, which can facilitate the exploration of unknown areas. Besides, a new quality evaluation scheme is developed to evaluate the quality of each solution in subspace, where the convergence and diversity of each solution is first measured, and in order to evaluate the diversity of solutions more finely, the global diversity and local diversity is designed to measure the diversity of each solution. Then, a dynamic penalty coefficient is designed to balance the convergence and diversity by penalizing the global diversity distribution of solutions. The performance of DAEA is validated by comparing with five state-of-the-art many-objective evolutionary algorithms on a number of well-known benchmark problems with up to 20 objectives. Experimental results show that our DAEA has high competitiveness in solving many-objective optimizatiopn problems compared with the other compared algorithms.https://www.aimspress.com/article/doi/10.3934/mbe.2023771?viewType=HTMLmany-objective optimizationdouble associationquality evaluationconvergencediversity |
| spellingShingle | Junhua Liu Wei Zhang Mengnan Tian Hong Ji Baobao Liu A double association-based evolutionary algorithm for many-objective optimization Mathematical Biosciences and Engineering many-objective optimization double association quality evaluation convergence diversity |
| title | A double association-based evolutionary algorithm for many-objective optimization |
| title_full | A double association-based evolutionary algorithm for many-objective optimization |
| title_fullStr | A double association-based evolutionary algorithm for many-objective optimization |
| title_full_unstemmed | A double association-based evolutionary algorithm for many-objective optimization |
| title_short | A double association-based evolutionary algorithm for many-objective optimization |
| title_sort | double association based evolutionary algorithm for many objective optimization |
| topic | many-objective optimization double association quality evaluation convergence diversity |
| url | https://www.aimspress.com/article/doi/10.3934/mbe.2023771?viewType=HTML |
| work_keys_str_mv | AT junhualiu adoubleassociationbasedevolutionaryalgorithmformanyobjectiveoptimization AT weizhang adoubleassociationbasedevolutionaryalgorithmformanyobjectiveoptimization AT mengnantian adoubleassociationbasedevolutionaryalgorithmformanyobjectiveoptimization AT hongji adoubleassociationbasedevolutionaryalgorithmformanyobjectiveoptimization AT baobaoliu adoubleassociationbasedevolutionaryalgorithmformanyobjectiveoptimization AT junhualiu doubleassociationbasedevolutionaryalgorithmformanyobjectiveoptimization AT weizhang doubleassociationbasedevolutionaryalgorithmformanyobjectiveoptimization AT mengnantian doubleassociationbasedevolutionaryalgorithmformanyobjectiveoptimization AT hongji doubleassociationbasedevolutionaryalgorithmformanyobjectiveoptimization AT baobaoliu doubleassociationbasedevolutionaryalgorithmformanyobjectiveoptimization |