[1]TAO Xinmin,XU Peng,LIU Furong,et al.Multi objective optimization algorithm composed of estimation of distribution and differential evolution[J].CAAI Transactions on Intelligent Systems,2013,8(1):39-45.[doi:10.3969/j.issn.1673-4785.201208035]
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Multi objective optimization algorithm composed of estimation of distribution and differential evolution

CAAI Transactions on Intelligent Systems[ISSN 1673-4785/CN 23-1538/TP] Volume: 8 Number of periods: 2013 1 Page number: 39-45 Column: 学术论文—智能系统 Public date: 2013-03-25
Title:
Multi objective optimization algorithm composed of estimation of distribution and differential evolution
Author(s):
TAO Xinmin 1;  XU Peng 1;  LIU Furong 2;  ZHANG Dongxue 1
1. College of Information and Communication Engineering, Harbin Engineering University, Harbin 150001, China;
2. Science and Information Department, Heilongjiang Electric Power Company Limited, Harbin 150090, China
Keywords:
multi objective optimization;  estimation of distribution algorithm;  differential evolution algorithm
CLC:
TP18
DOI:
10.3969/j.issn.1673-4785.201208035
Abstract:
In order to improve the ability of convergence and accuracy of a multi objective optimization algorithm, a multi objective optimization algorithm composed of estimation of distribution and differential evolution has been proposed. Both estimation of distribution algorithm and differential evolution algorithm will be used to generate particles of population. The generation method of each particle has been decided by using a selective factor, and proportion of the use of two algorithms according to the frequency of iterations. Utilizing an estimation of distribution algorithm to quickly locate in the initial search, and then differential evolution algorithm was used for accurately conducting searches. The variation factor of differential evolution algorithm was improved, and a variable variation factor also was defined and used to control the range of variation of differential evolution algorithm in different search periods. Four test functions were used to evaluate the performance of the proposed algorithm, and next the proposed algorithm was compared with nondominated sorting genetic algorithm II (NSGA II) and regularity model based multiobjective estimation of distribution algorithm (RM MEDA). The experimental results show that the proposed algorithm displayed a good convergence, diversity performance, and the stable effects.
References:
［1］周树德,孙增圻.分布估计算法综述［J］.自动化学报, 2007, 33(2): 113-124. 
ZHOU Shude, SUN Zengqi. A survey on estimation of distribution algorithms［J］. Acta Automatica Sinica, 2007, 33(2): 113-124.
［2］KHAN N, GOLDBERG D E, PELIKAN M. Multi objective Bayesian optimization algorithm［R］. Urbana, USA: University of Illinois at Urbana Champaign, 2002.
［3］OKABE T, JIN Y, SENDHOFF B, et al. Voronoi based estimation of distribution algorithm for multi objective optimization［C］//Proceedings of the 2004 Congress on Evolutionary Computation. Piscataway, USA, 2004: 1594-1601.
［4］SASTRY K, PELIKAN M, GOLDBERG D E. Decomposable problems, niching, and scalability of multiobjective estimation of distribution algorithms［R］. Urbana, USA: University of Illinois at Urbana Champaign, 2005.
［5］ZHANG Qingfu, ZHOU Aimin, JIN Yaochu. RM MEDA: a regularity model based multiobjective estimation of distribution algorithm［J］. IEEE Transactions on Evolutionary Computation, 2008, 12(1): 41-63.
［6］程玉虎,王雪松,郝名林.一种多样性保持的分布估计算法［J］.电子学报, 2010, 38(3): 591-597. 
CHENG Yuhu, WANG Xuesong, HAO Minglin. An estimation of distribution algorithm with diversity preservation［J］. Acta Electronica Sinica, 2010, 38(3): 591-597.
［7］STORN R, PRICE K. Differential evolution—a simple and efficient adaptive scheme for global optimization over continuous spaces［J］. Journal of Global Optimization, 1997, 11(4): 341-359.
［8］ROBIC T, FILIPIC B. DEMO: differential evolution for multiobjective optimization［C］//Proceedings of the 3rd International Conference on Evolutionary Multi Criterion Optimization. Berlin, Germany: Springer, 2005: 520-533.
［9］牛大鹏,王福利,何大阔,等.多目标混沌差分进化算法［J］.控制与决策, 2009, 24(3): 361-370. NIU Dapeng, WANG Fuli, HE Dakuo, et al. Chaotic differential evolution for multiobjective optimization［J］. Control and Decision, 2009, 24(3): 361-370.
［10］ZHAO Mengling, LIU Ruochen, LI Wenfeng, et al. Multi objective optimization based differential evolution constrained optimization algorithm［C］//Proceedings of 2010 Second WRI Global Congress on Intelligent Systems. Piscataway, USA, 2010: 320-326.
［11］李丽蓉,高卫峰.混合差分进化算法［J］.计算机工程与设计, 2012, 33(6): 2446 2450. LI Lirong, GAO Weifeng. Hybrid differential evolution algorithm［J］. Computer Engineering and Design, 2012, 33(6): 2446-2450.
［12］陶新民,刘玉,付丹丹,等.混合变异克隆选择多目标优化算法［J］.计算机仿真, 2011, 28(10): 199-203. 
TAO Xinmin, LIU Yu, FU Dandan, et al. Hybrid mutation clonal selection multiobjective optimization algorithm［J］. Computer Simulation, 2011, 28(10): 199-203.
［13］TSUTSUI S, PELIKAN M, GOLDBERG D E. Probabilistic model building genetic algorithms using marginal histograms in continuous domain［C］//Proceedings of the 5th International Conference on Knowledge Based Intelligent Information Engineering Systems and Allied Technology. Amsterdam, The Netherlands, 2001: 112-121.
［14］DEB K, PRATAP A, AGARWAL S, et al. A fast and elitist multiobjective genetic algorithm: NSGA II［J］. IEEE Transactions on Evolutionary Computation, 2002, 6(2): 182-197.
［15］ZITZLER E, DEB K, THIELE L. Comparison of multiobjective evolutionary algorithms: empirical results［J］. Evolutionary Computation, 2000, 8(2): 173-195.
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