A decomposition-based archiving approach for multi-objective evolutionary optimization
External archive can be used to improve the performance of a multi-objective evolutionary algorithm. Various archiving approaches have been developed but with some drawbacks. These drawbacks such as computation-inefficiency, retreating and shrinking, have not yet been well addressed. In this paper,...
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| Published in | Information sciences Vol. 430-431; pp. 397 - 413 |
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| Main Authors | , , , |
| Format | Journal Article |
| Language | English |
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Elsevier Inc
01.03.2018
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| Abstract | External archive can be used to improve the performance of a multi-objective evolutionary algorithm. Various archiving approaches have been developed but with some drawbacks. These drawbacks such as computation-inefficiency, retreating and shrinking, have not yet been well addressed. In this paper, we propose an efficient decomposition-based archiving approach (DAA) inspired from the decomposition strategy for dealing with multi-objective optimization. In DAA, the whole objective space is uniformly divided into a number of subspaces according to a set of weight vectors. At each generation, only one non-dominated solution lying in a subspace is chosen to be used for updating the external archive in consideration of its diversity. A normalized distance-based method, incorporated with the Pareto dominance, is proposed to decide which subspace a new solution should fall into, and whether this solution should replace existing one in this subspace or not. Empirical results on a diverse set of benchmark test problems show that DAA is more efficient than a number of state-of-the-art archiving methods in terms of the diversity of the obtained non-dominated solutions; and DAA can accelerate the convergence speed of the evolutionary search for most test problems. |
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| AbstractList | External archive can be used to improve the performance of a multi-objective evolutionary algorithm. Various archiving approaches have been developed but with some drawbacks. These drawbacks such as computation-inefficiency, retreating and shrinking, have not yet been well addressed. In this paper, we propose an efficient decomposition-based archiving approach (DAA) inspired from the decomposition strategy for dealing with multi-objective optimization. In DAA, the whole objective space is uniformly divided into a number of subspaces according to a set of weight vectors. At each generation, only one non-dominated solution lying in a subspace is chosen to be used for updating the external archive in consideration of its diversity. A normalized distance-based method, incorporated with the Pareto dominance, is proposed to decide which subspace a new solution should fall into, and whether this solution should replace existing one in this subspace or not. Empirical results on a diverse set of benchmark test problems show that DAA is more efficient than a number of state-of-the-art archiving methods in terms of the diversity of the obtained non-dominated solutions; and DAA can accelerate the convergence speed of the evolutionary search for most test problems. |
| Author | Sun, Jian-yong Qu, Bo-yang Gong, Dun-wei Zhang, Yong |
| Author_xml | – sequence: 1 givenname: Yong surname: Zhang fullname: Zhang, Yong organization: School of Information and Control Engineering, China University of Mining and Technology, 221116, China – sequence: 2 givenname: Dun-wei surname: Gong fullname: Gong, Dun-wei email: dwgong@vip.163.com organization: School of Information and Control Engineering, China University of Mining and Technology, 221116, China – sequence: 3 givenname: Jian-yong surname: Sun fullname: Sun, Jian-yong email: jy.sun@xjtu.edu.cn organization: School of Mathematics and Statistics, Xi'an Jiaotong University, Xi'an, China – sequence: 4 givenname: Bo-yang surname: Qu fullname: Qu, Bo-yang organization: School of Electrical and Information Engineering, Zhongyuan University of Technology, 450007, China |
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