求解多模态函数优化的微果蝇优化算法
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摘要
研究求解偏高维多模态函数优化的小种群果蝇优化算法.算法设计中,优质种群经局部变异探测优质个体;中等种群经精英个体引导实现个体转移;劣质种群依赖于精英和劣质个体沿着多方位搜寻多样个体.该算法具有结构简单、可调参数少、进化能力强等优点,其计算复杂度低.比较性的数值实验显示,此算法寻优能力强、搜索效率高且对偏高维函数优化问题具有较好应用潜力.
To solve the problem of higher-dimensional multi-modal function optimization,this work investigates a micro-population fly optimization algorithm. In the algorithm design,a local mutation strategy ensures the elitist sub-population to achieve strong exploitation,whereas the elitist individual identified in the process of evolution guides individuals included in the medium sub-population to transform towards specific directions. Moreover,the elitist and worst individuals help the inferior sub-population seek diverse and high-quality individuals along multiple directions. One such algorithm has the merits of structural simplicity,few parameters,strong evolution,and so on. Comparative numerical results show that the algorithm with strong global optimization and high efficiency has great potential for solving higher-dimensional function optimization problems.
To solve the problem of higher-dimensional multi-modal function optimization,this work investigates a micro-population fly optimization algorithm. In the algorithm design,a local mutation strategy ensures the elitist sub-population to achieve strong exploitation,whereas the elitist individual identified in the process of evolution guides individuals included in the medium sub-population to transform towards specific directions. Moreover,the elitist and worst individuals help the inferior sub-population seek diverse and high-quality individuals along multiple directions. One such algorithm has the merits of structural simplicity,few parameters,strong evolution,and so on. Comparative numerical results show that the algorithm with strong global optimization and high efficiency has great potential for solving higher-dimensional function optimization problems.
引文
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[11]程慧,刘成忠.基于混沌映射的混合果蝇优化算法[J].计算机工程,2013,39(5):218-221.Cheng H,Liu C Z.Mixed Fruit fly optimization algorithm based on chaotic mapping[J].Computer Engineering,2013,39(5):218-221.
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[17]韩俊英,刘成忠.反向认知的高效果蝇优化算法[J].计算机工程,2013,39(11):223-225.Han J Y,Liu C Z.Efficient fruit fly optimization algorithm with reverse cognition[J].Computer Engineering,2013,39(11):223-225.
[18]Pan Q K,Sang H Y,Duan J H,et al.An improved fruit fly optimization algorithm for continuous function optimization problems[J].Knowledge-Based Systems,2014,62:69-83.
[19]Wang L,Zheng X,Wang S.A novel binary fruit fly optimization algorithm for solving the multidimensional knapsack problem[J].KnowledgeBased Systems,2013,48:17-23.
[20]张维涛.果蝇优化SVM模型对有机化合物熔点的预测[J].广州化工,2015,43(5):128-130.Zhang W T.The prediction of organic compound melting point by SVM optimized by drosophila algorithm[J].Guangzhou Chemical Industry,2015,43(5):128-130.
[21]刘宇凯,胡志坚,索江镭,等.基于果蝇优化算法的广域阻尼控制器设计[J].电力系统保护与控制,2014,42(24):25-29.Liu Y K,Hu Z J,Suo J L,et al.Design method of wide-area damping controller based on FOA algorithm[J].Power System Protection and Control,2014,42(24):25-29.
[22]洪文鹏,廖明俊.基于果蝇优化支持向量机的风机故障诊断研究[J].风机技术,2014,6:50-54.Hong W P,Liao M J.Fault diagnosis of fan based on support vector machine of drosophila optimization[J].Compressor Blower&Fan Technology,2014,6:50-54.
[23]吴仲南,邹志华,杜永均.果蝇嗅觉分子机理研究进展[J].昆虫学报,2009,52(7):799-807.Wu Z N,Zou Z H,Du Y J.Proress in molecular mechanism of olfaction in drosophila melanogaster[J].Acta Entomologica Sinica,2009,52(7):799-807.
[24]冷雪,那杰.昆虫复眼的结构和功能[J].沈阳师范大学学报:自然科学版,2009,27(2):241-244.Leng X,Na J.Structure and function of insect compound eyes[J].Journal of Shenyang Normal University:Natural Science,2009,27(2):241-244.
[25]Derrac J,García S,Molina D,et al.A practical tutorial on the use of nonparametric statistical tests as a methodology for comparing evolutionary and swarm intelligence algorithms[J].Swarm&Evolutionary Computation,2011(1):3-18.
[26]Altman E I.Financial Ratios,Discriminant analysis and the prediction of corporate bankruptcy[J].Journal of Finance,1968,23(4):589-609.
[2]杨燕,靳蕃,Kamel M.微粒群优化算法研究现状及其进展[J].计算机工程,2004,30(21):3-4.Yang Y,Jin F,Kamel M.Research and development of particle swarm optimization[J].Computer Engineering,2004,30(21):3-4.
[3]刘晓峰.微粒群算法的收敛性及其在工程项目优化中的应用研究[D].天津:天津大学,2004.Liu X F.Research on convergence analysis of PSO and its application to project optimization[D].Tianjin:Tianjin University,2004.
[4]陈黎卿,张栋,陈无畏,等.基于微粒子群优化算法的差速器壳体轻量化设计[J].农业工程学报,2013,29(9):24-31.Chen L Q,Zhang D,Chen W W,et al.Lightweight design of differential case based on particle swarm optimization algorithm[J].Transactions of the Chinese Society of Agricultural Engineering,2013,29(9):24-31.
[5]潘文超.果蝇最佳化演算法-最新演化式计算技术[M].台北:沧海书局,2011:1-12.Pan W C.A new evolutionary computation approach:Fruit fly optimization algorithm[M].Taibei:Bookstore of Canghai,2011:1-12.
[6]Pan W T.A new fruit fly optimization algorithm:Taking the financial distress model as an example[J].Knowledge-Based Systems,2012,26:69-74.
[7]刘成忠,韩俊英.基于细菌迁徙的自适应果蝇优化算法[J].计算机工程与科学,2014,36(4):690-696.Liu C Z,Han J Y.Adaptive fruit fly optimization algorithm based on bacterial migration[J].Computer Engineering&Science,2014,36(4):690-696.
[8]贺智明,宋建国,梅宏标.结合元胞自动机的果蝇优化算法[J].计算机应用,2014,34(8):2295-2298.He Z M,Song J G,Mei H B.Fruit fly optimization algorithm based on cellular automata[J].Journal of Computer Application,2014,34(8):2295-2298.
[9]宁剑平,王冰,李洪儒,等.递减步长果蝇优化算法及应用[J].深圳大学学报:理工版,2014,31(4):367-373.Ning J P,Wang B,Li H R,et al.Research on and application of diminishing step fruit fly optimization algorithm[J].Journal of Shenzhen U-niversity:Science and Engineering,2014,31(4):367-373.
[10]刘成忠,黄高宝,张仁陟,等.局部深度搜索的混合果蝇优化算法[J].计算机应用,2014,34(4):1060-1064.Liu C Z,Huang G B,Zhang R Z,et al.Shuffled fruit fly optimization algorithm with local deep search[J].Journal of Computer Applications,2014,34(4):1060-1064.
[11]程慧,刘成忠.基于混沌映射的混合果蝇优化算法[J].计算机工程,2013,39(5):218-221.Cheng H,Liu C Z.Mixed Fruit fly optimization algorithm based on chaotic mapping[J].Computer Engineering,2013,39(5):218-221.
[12]杜军俊.果蝇优化算法的加权策略研究[J].微型机与应用,2014,33(16):67-70.Du J J.Study on the strategy of weighting in fruit fly optimization algorithm[J].Technique and Method,2014,33(16):67-70.
[13]Dai H,Zhao G,Lu J,et al.Comment and improvement on“A new fruit fly optimization algorithm:Taking the financial distress model as an example”[J].Knowledge-Based Systems,2014,59:159-160.
[14]Lei X,Du M,Xu J,et al.Chaotic fruit fly optimization algorithm[M]//Advances in Swarm Intelligence.Berlin,Germany:Springer International Publishing,2014:74-85.
[15]Yuan X,Dai X,Zhao J,et al.On a novel multiswarm fruit fly optimization algorithm and its application[J].Applied Mathematics and Computation,2014,233:260-271.
[16]韩俊英,刘成忠,王联国.动态双子群协同进化果蝇优化算法[J].模式识别与人工智能,2013,26(11):1057-1067.Han J Y,Liu C Z,Wang L G.Dynamic double subgroup cooperative fruit fly optimization algorithm[J].Pattern Recognition&Artificial Intelligence,2013,26(11):1057-1067.
[17]韩俊英,刘成忠.反向认知的高效果蝇优化算法[J].计算机工程,2013,39(11):223-225.Han J Y,Liu C Z.Efficient fruit fly optimization algorithm with reverse cognition[J].Computer Engineering,2013,39(11):223-225.
[18]Pan Q K,Sang H Y,Duan J H,et al.An improved fruit fly optimization algorithm for continuous function optimization problems[J].Knowledge-Based Systems,2014,62:69-83.
[19]Wang L,Zheng X,Wang S.A novel binary fruit fly optimization algorithm for solving the multidimensional knapsack problem[J].KnowledgeBased Systems,2013,48:17-23.
[20]张维涛.果蝇优化SVM模型对有机化合物熔点的预测[J].广州化工,2015,43(5):128-130.Zhang W T.The prediction of organic compound melting point by SVM optimized by drosophila algorithm[J].Guangzhou Chemical Industry,2015,43(5):128-130.
[21]刘宇凯,胡志坚,索江镭,等.基于果蝇优化算法的广域阻尼控制器设计[J].电力系统保护与控制,2014,42(24):25-29.Liu Y K,Hu Z J,Suo J L,et al.Design method of wide-area damping controller based on FOA algorithm[J].Power System Protection and Control,2014,42(24):25-29.
[22]洪文鹏,廖明俊.基于果蝇优化支持向量机的风机故障诊断研究[J].风机技术,2014,6:50-54.Hong W P,Liao M J.Fault diagnosis of fan based on support vector machine of drosophila optimization[J].Compressor Blower&Fan Technology,2014,6:50-54.
[23]吴仲南,邹志华,杜永均.果蝇嗅觉分子机理研究进展[J].昆虫学报,2009,52(7):799-807.Wu Z N,Zou Z H,Du Y J.Proress in molecular mechanism of olfaction in drosophila melanogaster[J].Acta Entomologica Sinica,2009,52(7):799-807.
[24]冷雪,那杰.昆虫复眼的结构和功能[J].沈阳师范大学学报:自然科学版,2009,27(2):241-244.Leng X,Na J.Structure and function of insect compound eyes[J].Journal of Shenyang Normal University:Natural Science,2009,27(2):241-244.
[25]Derrac J,García S,Molina D,et al.A practical tutorial on the use of nonparametric statistical tests as a methodology for comparing evolutionary and swarm intelligence algorithms[J].Swarm&Evolutionary Computation,2011(1):3-18.
[26]Altman E I.Financial Ratios,Discriminant analysis and the prediction of corporate bankruptcy[J].Journal of Finance,1968,23(4):589-609.