An Entropy-Based Multiobjective Evolutionary Algorithm with an Enhanced Elite Mechanism
Multiobjective optimization problem (MOP) is an important and challenging topic in the fields of industrial design and scientific research. Multi-objective evolutionary algorithm (MOEA) has proved to be one of the most efficient algorithms solving the multi-objective optimization. In this paper, we...
Saved in:
| Main Authors: | , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Wiley
2012-01-01
|
| Series: | Applied Computational Intelligence and Soft Computing |
| Online Access: | http://dx.doi.org/10.1155/2012/682372 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1832559365768347648 |
|---|---|
| author | Yufang Qin Junzhong Ji Chunnian Liu |
| author_facet | Yufang Qin Junzhong Ji Chunnian Liu |
| author_sort | Yufang Qin |
| collection | DOAJ |
| description | Multiobjective optimization problem (MOP) is an important and challenging topic in the fields of industrial design and scientific research. Multi-objective evolutionary algorithm (MOEA) has proved to be one of the most efficient algorithms solving the multi-objective optimization. In this paper, we propose an entropy-based multi-objective evolutionary algorithm with an enhanced elite mechanism (E-MOEA), which improves the convergence and diversity of solution set in MOPs effectively. In this algorithm, an enhanced elite mechanism is applied to guide the direction of the evolution of the population. Specifically, it accelerates the population to approach the true Pareto front at the early stage of the evolution process. A strategy based on entropy is used to maintain the diversity of population when the population is near to the Pareto front. The proposed algorithm is executed on widely used test problems, and the simulated results show that the algorithm has better or comparative performances in convergence and diversity of solutions compared with two state-of-the-art evolutionary algorithms: NSGA-II, SPEA2 and the MOSADE. |
| format | Article |
| id | doaj-art-9f35d3d758634b7082622c55e7c3ebf3 |
| institution | Kabale University |
| issn | 1687-9724 1687-9732 |
| language | English |
| publishDate | 2012-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Applied Computational Intelligence and Soft Computing |
| spelling | doaj-art-9f35d3d758634b7082622c55e7c3ebf32025-02-03T01:30:17ZengWileyApplied Computational Intelligence and Soft Computing1687-97241687-97322012-01-01201210.1155/2012/682372682372An Entropy-Based Multiobjective Evolutionary Algorithm with an Enhanced Elite MechanismYufang Qin0Junzhong Ji1Chunnian Liu2Beijing Municipal Key Laboratory of Multimedia and Intelligent Software Technology, College of Computer Science and Technology, Beijing University of Technology, Beijing 100124, ChinaBeijing Municipal Key Laboratory of Multimedia and Intelligent Software Technology, College of Computer Science and Technology, Beijing University of Technology, Beijing 100124, ChinaBeijing Municipal Key Laboratory of Multimedia and Intelligent Software Technology, College of Computer Science and Technology, Beijing University of Technology, Beijing 100124, ChinaMultiobjective optimization problem (MOP) is an important and challenging topic in the fields of industrial design and scientific research. Multi-objective evolutionary algorithm (MOEA) has proved to be one of the most efficient algorithms solving the multi-objective optimization. In this paper, we propose an entropy-based multi-objective evolutionary algorithm with an enhanced elite mechanism (E-MOEA), which improves the convergence and diversity of solution set in MOPs effectively. In this algorithm, an enhanced elite mechanism is applied to guide the direction of the evolution of the population. Specifically, it accelerates the population to approach the true Pareto front at the early stage of the evolution process. A strategy based on entropy is used to maintain the diversity of population when the population is near to the Pareto front. The proposed algorithm is executed on widely used test problems, and the simulated results show that the algorithm has better or comparative performances in convergence and diversity of solutions compared with two state-of-the-art evolutionary algorithms: NSGA-II, SPEA2 and the MOSADE.http://dx.doi.org/10.1155/2012/682372 |
| spellingShingle | Yufang Qin Junzhong Ji Chunnian Liu An Entropy-Based Multiobjective Evolutionary Algorithm with an Enhanced Elite Mechanism Applied Computational Intelligence and Soft Computing |
| title | An Entropy-Based Multiobjective Evolutionary Algorithm with an Enhanced Elite Mechanism |
| title_full | An Entropy-Based Multiobjective Evolutionary Algorithm with an Enhanced Elite Mechanism |
| title_fullStr | An Entropy-Based Multiobjective Evolutionary Algorithm with an Enhanced Elite Mechanism |
| title_full_unstemmed | An Entropy-Based Multiobjective Evolutionary Algorithm with an Enhanced Elite Mechanism |
| title_short | An Entropy-Based Multiobjective Evolutionary Algorithm with an Enhanced Elite Mechanism |
| title_sort | entropy based multiobjective evolutionary algorithm with an enhanced elite mechanism |
| url | http://dx.doi.org/10.1155/2012/682372 |
| work_keys_str_mv | AT yufangqin anentropybasedmultiobjectiveevolutionaryalgorithmwithanenhancedelitemechanism AT junzhongji anentropybasedmultiobjectiveevolutionaryalgorithmwithanenhancedelitemechanism AT chunnianliu anentropybasedmultiobjectiveevolutionaryalgorithmwithanenhancedelitemechanism AT yufangqin entropybasedmultiobjectiveevolutionaryalgorithmwithanenhancedelitemechanism AT junzhongji entropybasedmultiobjectiveevolutionaryalgorithmwithanenhancedelitemechanism AT chunnianliu entropybasedmultiobjectiveevolutionaryalgorithmwithanenhancedelitemechanism |