Opposition-based Learning Teaching-learning-based Optimization Algorithm with a Micro Population and Its Application

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Opposition-based Learning Teaching-learning-based Optimization Algorithm with a Micro Population and Its Application

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[1]FAN Qinqin,LIU Dixizi,WANG Xiaowei,et al.Opposition-based Learning Teaching-learning-based Optimization Algorithm with a Micro Population and Its Application[J].Journal of Zhengzhou University (Engineering Science),2020,41(04):59-67.[doi:10.13705/j.issn.1671-6833.2020.01.020]

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Journal of Zhengzhou University (Engineering Science)[ISSN 1671-6833/CN 41-1339/T] Volume: 41 Number of periods: 2020 04 Page number: 59-67 Column: Public date: 2020-08-12

Title:: Opposition-based Learning Teaching-learning-based Optimization Algorithm with a Micro Population and Its Application

Author(s):: FAN Qinqin1; 2; LIU Dixizi1; WANG Xiaowei1; HAN Xin3; WANG Weili1; 1.Logistics Research Center,Shanghai Maritime University,Shanghai 201306,China;2.School of Electronic Information and Electrical Engineering,Shanghai Jiao Tong University,Shanghai 200237,China;3.Shanghai Institute of Disaster Prevention and Relief,Tongji University,Shanghai 200092,China

Keywords:: teaching-learning-based optimization; micro population; opposition-based learning; non-cooperative game

CLC:: -

DOI:: 10.13705/j.issn.1671-6833.2020.01.020

Abstract:: To improve the convergence speed and reliability of Teaching-learning-based optimization (TLBO) algorithm, an opposition-based learning TLBO with a micro population (OBL-μTLBO) is proposed in the current study. In the proposed algorithm, a micro population is used to speed up the convergence and an opposition-based learning is utilized to improve the global exploration capability of TLBO. Simulation results indicate that OBL-μTLBO not only has better overall performance, but also has more quick convergence speed when compared with other competitors. Finally, OBL- μTLBO is used to solve two Nash equilibrium problems of non-cooperative game, and satisfactory results are achieved.

References:: [1] RAO R V,SAVSANI V J,VAKHARIA D P.Teaching-learning-based optimization:an optimization method for continuous non-linear large scale problems[J].Information sciences,2012,183(1):1-15.
[2] RAO R V,SAVSANI V J,VAKHARIA D P.Teaching-learning-based optimization:a novel method for constrained mechanical design optimization problems[J].Computer-aided design,2011,43(3):303-315.
[3] 于坤杰,王昕,王振雷.基于反馈的精英教学优化算法[J].自动化学报,2014,40(9):1976-1983.
[4] MALLIPEDDI R,SUGANTHAN P N.Empirical study on the effect of population size on differential evolution algorithm[C]//IEEE Congress on Evolutionary Computation.Hong Kong:IEEE,2008:3663-3670.
[5] REN X,CHEN Z Z,MA Z.Differential evolution using smaller population[C]//Second International Conference on Machine Learning and Computing.Bangalore:IEEE,2010:76-80.
[6] BROWN C,JIN Y C,LEACH M,et al.μJADE:adaptive differential evolution with a small population[J].Soft computing,2016,20(10):4111-4120.
[7] GONG W Y,CAI Z H,WANG Y.Repairing the crossover rate in adaptive differential evolution[J].Applied soft computing,2014,15:149-168.
[8] MARQUEZ-GRAJALES A,MEZURA-MONTES E.μJADEε:micro adaptive differential evolution to solve constrained optimization problems[C]// IEEE Congress on Evolutionary Cmputation.Vancouver:IEEE,2016:4183-4190.
[9] SALEHINEJAD H,RAHNAMAYAN S,TIZHOOSH H R,et al.Micro-differential evolution with vectorized random mutation factor[C]// IEEE Congress on Evolutionary Computation.Beijing:IEEE,2014:2055-2062.
[10] NASH J.Non-cooperative games[J].The annals of mathematics,1951,54(2):286.
[11] 陈士俊,孙永广,吴宗鑫.一种求解NASH均衡解的遗传算法[J].系统工程,2001,19(5):67-70.
[12] 邱中华,高洁,朱跃星.应用免疫算法求解博弈问题[J].系统工程学报,2006,21(4):398-404.
[13] 贾文生,向淑文,杨剑锋,等.基于免疫粒子群算法的非合作博弈Nash均衡问题求解[J].计算机应用研究,2012,29(1):28-31.
[14] 王志勇,韩旭,许维胜,等.基于改进蚁群算法的纳什均衡求解[J].计算机工程,2010,36(14):166-168,171.
[15] TIZHOOSH H R.Opposition-based learning:a new scheme for machine intelligence[C]// International Conference on Computational Intelligence for Modelling,Control and Automation and International Conference on Intelligent Agents,Web Technologies and Internet Commerce.Vienna:IEEE,2005.
[16] WANG W J,WANG H,SUN H,et al.Using opposition-based learning to enhance differential evolution:a comparative study[C]// IEEE Congress on Evolutionary Computation.Vancouver:IEEE,2016:71-77.
[17] WANG H,WU Z J,RAHNAMAYAN S,et al.Enhancing particle swarm optimization using generalized opposition-based learning[J].Information sciences,2011,181(20):4699-4714.
[18] CHEN X,YU K J,DU W L,et al.Parameters identification of solar cell models using generalized oppositional teaching learning based optimization[J].Energy,2016,99:170-180.[19] 柳缔西子,范勤勤,胡志华.基于混沌搜索和权重学习的教与学优化算法及其应用[J].智能系统学报,2018,13(5):818-828.
[20] SHARMA H,SHRIVASTAVA P,BANSAL J C,et al.Fitness based selfadaptive differential evolution[M]//Nature Inspired Cooperative Strategies for Optimization(NICSO 2013).Cham:Springer International width=60,height=13,dpi=110 2014:71-84.
[21] RAHNAMAYAN S,TIZHOOSH H R,SALAMA M M A.Opposition-based differential evolution[J].IEEE transactions on evolutionary computation,2008,12(1):64-79.
[22] FAJFAR I,TUMA T,PUHAN J,et al.Towards smaller populations in differential evolution[J].Journal of microelectronics,electronic components and materials,2012,42(3):152-163.[23] FRIEDMAN M.The use of ranks to avoid the assumption of normality implicit in the analysis of variance[J].Journal of the emerican statistical association,1939,34(205):109.
[24] DUNN O J.Multiple comparisons among means[J].Journal of the american statistical association,1961,56(293):52-64.
[25] GARCíA S,MOLINA D,LOZANO M,et al.A study on the use of non-parametric tests for analyzing the evolutionary algorithms′ behaviour:a case study on the CEC′ 2005 special session on real parameter optimization[J].Journal of heuristics,2009,15(6):617-644.
[26] WEIBULL J W.Evolutionary game theory[M].Cambridge:MIT Press,1997.
[27] WOLPERT D H,MACREADY W G.No free lunch theorems for optimization[J].IEEE transactions on evolutionary computation,1997,1(1):67-82.
[28] BREST J,GREINER S,BOSKOVIC B,et al.Self-adapting control parameters in differential evolution:a comparative study on numerical benchmark problems[J].IEEE transactions on evolutionary computation,2006,10(6):646-657.
[29] QIN A K,HUANG V L,SUGANTHAN P N.Differential evolution algorithm with strategy adaptation for global numerical optimization[J].IEEE transactions on evolutionary computation,2009,13(2):398-417.
[30] LIANG J J,QIN A K,SUGANTHAN P N,et al.Comprehensive learning particle swarm optimizer for global optimization of multimodal functions[J].IEEE transactions on evolutionary computation,2006,10(3):281-295.
[31] WILCOXON F.Individual comparisons by ranking methods[J].Biometrics bulletin,1945,1(6):80.

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