DCDG-EA: Dynamic convergence–diversity guided evolutionary algorithm for many-objective optimization
•DCDG-EA algorithm uses reference vector decomposition to solve MaOPs.•CDOS selects an appropriate operator to generate offspring.•CDIS strategy simultaneously considers the convergence and diversity of solutions. Maintaining a good balance between the convergence and the diversity is particularly c...
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| Vydáno v: | Expert systems with applications Ročník 118; s. 35 - 51 |
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| Hlavní autoři: | , , , , |
| Médium: | Journal Article |
| Jazyk: | angličtina |
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New York
Elsevier Ltd
15.03.2019
Elsevier BV |
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| ISSN: | 0957-4174, 1873-6793 |
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| Abstract | •DCDG-EA algorithm uses reference vector decomposition to solve MaOPs.•CDOS selects an appropriate operator to generate offspring.•CDIS strategy simultaneously considers the convergence and diversity of solutions.
Maintaining a good balance between the convergence and the diversity is particularly crucial for the performance of the evolutionary algorithms (EAs). However, the traditional multi-objective evolutionary algorithms, which have shown their competitive performance with a variety of practical problems involving two or three objectives, face significant challenges in case of problems with more than three objectives, namely many-objective optimization problems (MaOPs). This paper proposes a dynamic convergence–diversity guided evolutionary algorithm, namely (DCDG-EA) for MaOPs by employing the decomposition technique. Besides, the objective space of MaOPs is divided into K subspaces by a set of uniformly distributed reference vectors. Each subspace has its own subpopulation and evolves in parallel with the other subspaces. In DCDG-EA, the balance between the convergence and the diversity is achieved through the convergence–diversity based operator selection (CDOS) strategy and convergence–diversity based individual selection (CDIS) strategy. In CDOS, for each operator of the set of operators, a selection probability is assigned which is related to its convergence and diversity capabilities. Based on the attributed selection probabilities, an appropriate operator is selected to generate the offsprings. Furthermore, CDIS is used which allows to greatly overcome the inefficiency of the Pareto dominance approaches. It updates each subpopulation by using two independent distance measures that represent the convergence and the control diversity, respectively. The experimental results on DTLZ and WFG benchmark problems with up to 15 objectives show that our algorithm is highly competitive comparing with the four state-of-the-art evolutionary algorithms in terms of convergence and diversity. |
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| AbstractList | Maintaining a good balance between the convergence and the diversity is particularly crucial for the performance of the evolutionary algorithms (EAs). However, the traditional multi-objective evolutionary algorithms, which have shown their competitive performance with a variety of practical problems involving two or three objectives, face significant challenges in case of problems with more than three objectives, namely many-objective optimization problems (MaOPs). This paper proposes a dynamic convergence–diversity guided evolutionary algorithm, namely (DCDG-EA) for MaOPs by employing the decomposition technique. Besides, the objective space of MaOPs is divided into K subspaces by a set of uniformly distributed reference vectors. Each subspace has its own subpopulation and evolves in parallel with the other subspaces. In DCDG-EA, the balance between the convergence and the diversity is achieved through the convergence–diversity based operator selection (CDOS) strategy and convergence–diversity based individual selection (CDIS) strategy. In CDOS, for each operator of the set of operators, a selection probability is assigned which is related to its convergence and diversity capabilities. Based on the attributed selection probabilities, an appropriate operator is selected to generate the offsprings. Furthermore, CDIS is used which allows to greatly overcome the inefficiency of the Pareto dominance approaches. It updates each subpopulation by using two independent distance measures that represent the convergence and the control diversity, respectively. The experimental results on DTLZ and WFG benchmark problems with up to 15 objectives show that our algorithm is highly competitive comparing with the four state-of-the-art evolutionary algorithms in terms of convergence and diversity. •DCDG-EA algorithm uses reference vector decomposition to solve MaOPs.•CDOS selects an appropriate operator to generate offspring.•CDIS strategy simultaneously considers the convergence and diversity of solutions. Maintaining a good balance between the convergence and the diversity is particularly crucial for the performance of the evolutionary algorithms (EAs). However, the traditional multi-objective evolutionary algorithms, which have shown their competitive performance with a variety of practical problems involving two or three objectives, face significant challenges in case of problems with more than three objectives, namely many-objective optimization problems (MaOPs). This paper proposes a dynamic convergence–diversity guided evolutionary algorithm, namely (DCDG-EA) for MaOPs by employing the decomposition technique. Besides, the objective space of MaOPs is divided into K subspaces by a set of uniformly distributed reference vectors. Each subspace has its own subpopulation and evolves in parallel with the other subspaces. In DCDG-EA, the balance between the convergence and the diversity is achieved through the convergence–diversity based operator selection (CDOS) strategy and convergence–diversity based individual selection (CDIS) strategy. In CDOS, for each operator of the set of operators, a selection probability is assigned which is related to its convergence and diversity capabilities. Based on the attributed selection probabilities, an appropriate operator is selected to generate the offsprings. Furthermore, CDIS is used which allows to greatly overcome the inefficiency of the Pareto dominance approaches. It updates each subpopulation by using two independent distance measures that represent the convergence and the control diversity, respectively. The experimental results on DTLZ and WFG benchmark problems with up to 15 objectives show that our algorithm is highly competitive comparing with the four state-of-the-art evolutionary algorithms in terms of convergence and diversity. |
| Author | Jiang, Shilong Nouioua, Mourad Gu, Yu Lin, Ke Li, Zhiyong |
| Author_xml | – sequence: 1 givenname: Zhiyong orcidid: 0000-0001-9720-5915 surname: Li fullname: Li, Zhiyong email: zhiyong.li@hnu.edu.cn organization: Key Laboratory for Embedded and Network Computing of Hunan Province 410082, College of Computer Science and Electronic Engineering of Hunan University, Changsha 410082, China – sequence: 2 givenname: Ke surname: Lin fullname: Lin, Ke email: kelin_0808@hnu.edu.cn organization: Key Laboratory for Embedded and Network Computing of Hunan Province 410082, College of Computer Science and Electronic Engineering of Hunan University, Changsha 410082, China – sequence: 3 givenname: Mourad surname: Nouioua fullname: Nouioua, Mourad email: mouradnouioua@hnu.edu.cn organization: Key Laboratory for Embedded and Network Computing of Hunan Province 410082, College of Computer Science and Electronic Engineering of Hunan University, Changsha 410082, China – sequence: 4 givenname: Shilong surname: Jiang fullname: Jiang, Shilong email: jiangshilong03@126.com organization: PKU-HKUST Shenzhen-HongKong Institution, Shenzhen 518057, China – sequence: 5 givenname: Yu surname: Gu fullname: Gu, Yu email: guyu@mail.buct.edu.cn organization: Beijing Advanced Innovation Center for Soft Matter Science and Engineering, Beijing University of Chemical Technology, Beijing 100029, China |
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| Keywords | Pareto optimality Decomposition Many-objective optimization Evolutionary algorithm Diversity Convergence |
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| SubjectTerms | Algorithms Convergence Decomposition Distance measurement Diversity Evolutionary algorithm Evolutionary algorithms Many-objective optimization Multiple objective analysis Optimization Pareto optimality State of the art Subspaces |
| Title | DCDG-EA: Dynamic convergence–diversity guided evolutionary algorithm for many-objective optimization |
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