基于记忆原理的人际关系优化算法
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摘要
为求解复杂函数优化问题,基于人类记忆原理和人际关系,提出了一种新型函数优化方法,即MP-IRO算法。在该算法中,将个体分为恋人、知己、敌人、小人、陌生人5种对象类型,对应于恋爱、聚集、攻击、排斥、防御等5种行为,并构造相应的演化算子。恋爱算子能优先选择拥有长时记忆的个体,分享其表征特性;聚集算子能使个体摆脱局部最优解的陷阱;攻击算子能使个体之间活跃度增强;排斥算子能让个体远离瞬时记忆试探解方向,扩大搜索范围;防御算子能增加随机性。测试结果表明,本算法对求解复杂函数优化问题具有较高的适应性和收敛速度。
In order to solve the complicated function optimization problems, a new optimization algorithm was constructed based on the memory principles(MP) and interpersonal relationship(IR), namely memory principles-based interpersonal relationship optimization(MP-IRO). There are five object types in interpersonal relationship system, including lovers, friends, enemies, villains and strangers which successively correspond to loving, gathering, attacking, rejecting, defending instincts and five operators; the loving operator gives preference to individuals which memory type is long memory(LM) and shares CP with other individuals; the gathering operator could make individuals avert local optima; the attacking operator enables an individual's vitality to increase; the rejecting operator can be far from instantaneous memory to expand the search coverage; the defending operator increases the randomness. Results show that the algorithm has characteristics of strong search capability and high adaptability for the complicated function optimization problems.
In order to solve the complicated function optimization problems, a new optimization algorithm was constructed based on the memory principles(MP) and interpersonal relationship(IR), namely memory principles-based interpersonal relationship optimization(MP-IRO). There are five object types in interpersonal relationship system, including lovers, friends, enemies, villains and strangers which successively correspond to loving, gathering, attacking, rejecting, defending instincts and five operators; the loving operator gives preference to individuals which memory type is long memory(LM) and shares CP with other individuals; the gathering operator could make individuals avert local optima; the attacking operator enables an individual's vitality to increase; the rejecting operator can be far from instantaneous memory to expand the search coverage; the defending operator increases the randomness. Results show that the algorithm has characteristics of strong search capability and high adaptability for the complicated function optimization problems.
引文
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[18]黄光球,李涛,陆秋琴.人工记忆优化算法[J].系统工程理论与实践,2014,34(1):1-13.(Huang Guangqiu,Li Tao,Lu Qiuqin.Artificial memory-based optimization.[J].System Engineering-Theory&Practice,2014,34(1):1-13.)
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[2]王凌.智能优化算法及其应用[M].北京:清华大学出版社,2001.(Wang Ling.Intelligent optimization algorithm and application[M].Beijing,China:Tsinghua University Press,2001.)
[3]蒋腾旭.智能优化算法概述[J].电脑知识与技术(学术交流),2007,8:507-530.(Jiang Tengxu.A summary of intelligence optimum algorithm[J].Computer Knowledge and Technology(Academic Exchange),2007,8:507-530.)
[4]马国强,刘娟.育种遗传算法及函数优化应用[J].小型微型计算机系统,2011,32(1):185-188.(Ma Guoqiang,Liu Juan.Breed genetic algorithm and application of function optimization[J].Journal of Chinese Computer System,2011,32(1):185-188.)
[5]Manoj Thakur,Suraj S Meghwani,Hemant Jalota.A modified real coded genetic algorithm for constrained optimization[J].Applied Mathematics and Computation(S0096-3003),2014,235(25):292-317.
[6]Bahriye Akay,Dervis Karaboga.A modified Artificial Bee Colony algorithm for real-parameter optimization[J].Information Sciences(S0020-0255),2012,192(6):120-142.
[7]Marjan Mernik,Shih-Hsi Liu,Dervis Karaboga,et al.On clarifying misconceptions when comparing variants of the Artificial Bee Colony Algorithm by offering a new implementation[J].Information Sciences(S0020-0255),2015,291(10):115-127.
[8]Eberhart R,Kennedy J.New optimizer using particle swarm theory[C]//Proc of the 6th International Symposium on Micro Machine and Human Science.Piscataway,NJ,USA:IEEE Press,1995:38-43.
[9]崔志华,曾建潮.微粒群优化算法[M].北京:科学出版社,2011.(Cui Zhanghua,Zeng Jianchao.Particle swarm optimization algorithm[M].Beijing,China:Science Press,2011.)
[10]Xiangtao Li,Minghao Yin.Modified cuckoo search algorithm with self adaptive parameter method[J].Information Sciences(S0020-0255),2015,298(3):80-97.
[11]Gamperle R,Muller S D,Koumoutsakos A,et al.A Parameter Study for Differential Evolution[C]//Wseas Int Conf on Advances in Intelligent Systems,Fuzzy Systems,Evolutionary Computation.Switzerland:WSEAS,2002,10(10):293-298.
[12]贾丽媛,张弛.自适应差分演化算法[J].中南大学学报(自然科学版),2013,44(9):3759-3765.(Jia Liyuan,Zhang Chi.Self-adaptive differential evolution[J].Journal of Central South University(Science and Technology),2013,44(9):3759-3765.)
[13]Simon D.Biogeography-based optimization[J].IEEE Trans on Evolutionary Computation(S1089-778X),2008,12(6):702-713.
[14]Daniel L.Greenberg.Flashbulb memories[J].International Encyclopedia of the Social&Behavioral Sciences,2015,5(12):254-259.
[15]左赛哲,郭玉翠,宫尚宝,等.基于记忆原理的信任值更新模型[J].东南大学学报(自然科学版),2010,40(2):307-312.(Zuo Saizhe,Guo Yucui,Gong Shangbao,et al.Trust value update model based on the memory theory[J].Journal of Southeast University(Natural Science Edition),2010,40(2):307-312.)
[16]熊万强,王蓓莉,孙晓光.基于生物记忆原理的智能词汇记忆模型[J].计算机工程.2015,41(6):254-257.(Xiong Wanqiang,Wang Beili,Sun Xiaoguang.Intelligent vocabulary memory model based on biological memory principle[J].Computer Engineering,2015,41(6):254-257.)
[17]黄光球,邢玉飞,赵煜.蚁群智能体记忆模型研究[J].计算机工程与应用,2010,46(27):46-51.(Huang Guangqiu,Xing Yufei,Zhao Yu.Research on memory model of ant colony agent[J].Computer Engineering and Applications,2010,46(27):46-51.)
[18]黄光球,李涛,陆秋琴.人工记忆优化算法[J].系统工程理论与实践,2014,34(1):1-13.(Huang Guangqiu,Li Tao,Lu Qiuqin.Artificial memory-based optimization.[J].System Engineering-Theory&Practice,2014,34(1):1-13.)
[19]艾森克,基恩.认知心理学[M].5版.上海:华东师范大学出版社,2009.(Eysenck M W,Keehn M T.Cognitive Psychology[M].5th ed.Shanghai,China:East China Normal University Press,2009.)
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