A Weight Design Method Based on Power Transformation for Multi-Objective Evolutionary Algorithm MOEA/D

Liu Hailin; Gu Fangqing; Cheung Yiuming

Journal of Computer Research and Development > 2012 > 49(6): 1264-1271.

Liu Hailin, Gu Fangqing, Cheung Yiuming. A Weight Design Method Based on Power Transformation for Multi-Objective Evolutionary Algorithm MOEA/D[J]. Journal of Computer Research and Development, 2012, 49(6): 1264-1271.

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A Weight Design Method Based on Power Transformation for Multi-Objective Evolutionary Algorithm MOEA/D

1(School of Applied Mathematics, Guangdong University of Technology, Guangzhou 510006) 2(Department of Computer Science, Hong Kong Baptist University, Hong Kong)

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Published Date: June 14, 2012

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Abstract

Abstract

In multi-objective optimization problems, it is very important to find a group of uniformly distributed Pareto optimal solutions on Pareto fronts. MOEA/D is one of the promising evolutionary algorithms for multi-objective optimization at present. When the Pareto front is some known types of shape, the MOEA/D can find uniformly distributed Pareto-optimal solutions by using the advanced decomposition. Nevertheless, it is a nontrivial task for the MOEA/D for a general shape of the Pareto front. In this paper, each objective function is transformed by the power function, which makes the Pareto front of multi-objective optimization close to the desired shape. Furthermore, a kind of weight design method of MOEA/D is proposed to solve general multi-objective optimization problem. This paper also discusses the distance preserving character of Pareto front by mathematics transform. MOEA/D, making use of proposed weight design method, easily finds uniformly distributed Pareto optimal solutions for general multi-objective optimization problem. Numerical results show the effectiveness of MOEA/D with the proposed weight design method.
- multi-objective optimization,
- evolutionary algorithm,
- uniform distribution,
- Pareto front,
- weight design,
- power transformation

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[1]	Guan Xiaoqiang, Wang Wenjian, Pang Jifang, Meng Yinfeng. Space Transformation Based Random Forest Algorithm[J]. Journal of Computer Research and Development, 2021, 58(11): 2485-2499. DOI: 10.7544/issn1000-1239.2021.20200523
[2]	Lin Suzhen, Zhu Xiaohong, Wang Dongjuan, Wang Xiaoxia. Multi-Band Image Fusion Based on Embedded Multi-Scale Transform[J]. Journal of Computer Research and Development, 2015, 52(4): 952-959. DOI: 10.7544/issn1000-1239.2015.20131736
[3]	Wang Changjing. Verifying the Correctness of Loop Optimization Based on Extended Logic Transformation System μTS[J]. Journal of Computer Research and Development, 2012, 49(9): 1863-1873.
[4]	Liu Duo, Dai Yiqi. Construction of Transformation Matrix with a Given Period Modulo N[J]. Journal of Computer Research and Development, 2012, 49(5): 925-931.
[5]	Fang Wei, Sun Guangzhong, Wu Chao, and Chen Guoliang. A Parallel Algorithm of Three-Dimensional Fast Fourier Transform[J]. Journal of Computer Research and Development, 2011, 48(3): 440-446.
[6]	Zhao Xiaoming, Ye Xijian. A New Approach to Ridgelet Transform[J]. Journal of Computer Research and Development, 2008, 45(5): 915-922.
[7]	Wen Guihua. Relative Transformation for Machine Learning[J]. Journal of Computer Research and Development, 2008, 45(4): 612-618.
[8]	Tang Daquan, He Mingke, and Meng Qingsong. Research on Searching in Unstructured P2P Network Based on Power-Law Distribution and Small World Character[J]. Journal of Computer Research and Development, 2007, 44(9): 1566-1571.
[9]	Ming Xing, Liu Yuanning, Zhu Xiaodong, Xu Tao. Iris Recognition Based on Wavelet Transform with Shift Invariance Preprocessing[J]. Journal of Computer Research and Development, 2006, 43(7): 1186-1193.
[10]	Jia Jian, Jiao Licheng. Implementation of Digital Ridgelet Transform and a New Method[J]. Journal of Computer Research and Development, 2006, 43(1): 115-119.