计算机科学 ›› 2020, Vol. 47 ›› Issue (4): 194-203.doi: 10.11896/jsjkx.190200273
黄光球, 陆秋琴
HUANG Guang-qiu, LU Qiu-qin
摘要: 为了求解一类复杂非线性优化问题的全局最优解,基于采用垂直结构群落动力学理论,提出了一种新的垂直结构群落系统优化算法,简称为VS-CSO算法。该算法将优化问题的搜索空间视为一个生态系统,该生态系统具有若干个垂直结构分叉营养水平,在各个营养水平中生活着不同种类的生物种群;在每个种群内,有若干生物个体在活动;生物个体不能跨种群迁移,但在同类种群中会相互影响。各种群以循环捕食-被食或资源-消耗连接在一起。运用垂直结构群落动力学模型开发出了通吃算子、择食算子、干扰算子、侵染算子、新生算子、死亡算子。其中,通吃算子和择食算子可实现个体跨种群的信息交换,而干扰算子和侵染算子可实现种群内部个体之间的信息交换,从而确保个体间信息的充分交换;新生算子可适时补充新个体到种群中,而死亡算子可将种群中的虚弱个体适时清除掉,从而大幅提升算法跳出局部陷阱的能力。在求解过程中,VS-CSO算法每次只对极少变量进行处理,因此可求解高维优化问题。测试结果表明,VS-CSO算法能求解一类非常复杂的单峰函数、多峰函数和复合函数优化问题,其求精能力、探索能力及两者的协调性均优良,且具有全局收敛性的特点。该算法为求解一些较高维复杂函数优化问题的全局最优解提供了可行方案。
中图分类号:
[1]MIGUEL ANTONIO L,COELLO CARLOS A.Coevolutionary Multiobjective Evolutionary Algorithms:Survey of the State-of-the-Art[J].IEEE Transactions on Evolutionary Computation,2018,22(6):851-865. [2]CHUANG Y C,CHEN C T,HWANG C.A simple and efficient real-coded genetic algorithm for constrained optimization[J].Applied Soft Computing,2016,38(1):87-105. [3]WANG D P,HU K Y,MA L B,et al.Multispecies Coevolution Particle Swarm Optimization Based on Previous Search History[J].Discrete Dynamics in Nature and Society,2017,51(9):301-318. [4]AL-ROOMI A R,EL-HAWARY M E.Metropolis biogeography-based optimization[J].Information Sciences,2016,360(5):73-95. [5] MUKHERJEE R,DEBCHOUDHURY S,DAS S.Modified differential evolution with locality induced genetic operators for dynamic optimization[J].European Journal of Operational Research,2016,253:337-355. [6]SOUZA S S F,ROMERO R,PEREIRA J,et al.Artificial immune algorithm applied to distribution system reconfiguration with variable demand[J].Electrical Power and Energy Systems,2016,82(5):561-568. [7]HUANG G Q,LIU J F,YAO Y X.Global convergence proof of artificial fish swarm algorithm[J].Computer Engineering,2012,38(2):204-210. [8]KOROEC P,ILC J,FILIPIC B.The differential ant-stigmergy algorithm[J].Information Sciences,2012,192(5):82-97. [9]WANG S Q,WANG W X,XU H G.Theory and Method ofMathematical Ecology Stability[M].Beijing:Science Press,2004:297-332. [10]CHEN L S,MENG X Z,JIAO J J.Biodynamics [M].Beijing:Science Press,2009:77-174. [11]IISUFESCU M.Finite Markov Processes and Their Applications[M].Wiley:Chichester,1980. [12]LIANG J J,QU B Y,SUGANTHAN P N,et al.Problem Definitions and Evaluation Criteria for the CEC 2013 Special Session on Real-Parameter Optimization,Nanyang Technological University[OL].http://www.ntu.edu.sg/home/epnsugan/index_files/cec-2013/Definitions of CEC 13 benchmark suite 0117.pdf. [13]BEHESHTI Z,SHAMSUDDIN S M.Non-parametric particleswarm optimization for global optimization[J].Applied Soft Computing,2015,28(5):345-359. [14]ZHAO Z W,YANG J M,HU Z Y,et al.A differential evolution algorithm with self-adaptive strategy and control parameters based on symmetric Latin hypercube design for unconstrained optimization problems[J].European Journal of Operational Research,2016,250(1):30-45. [15]LI G H,CUI L Z,FU X H,et al.Artificial bee colony algorithm with gene recombination for numerical function optimization[J].Applied Soft Computing,2017,52(7)146-159. [16] CˇREPINEK M,LIU S H,MERNIK M.Replication and comparison of computational experiments in applied evolutionary computing:Common pitfalls and guidelines to avoid them[J].Applied Soft Computing,2014,19:161-170. [17]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 and Evolutionary Computation,2011,1:3-18. |
[1] | 张新明, 李双倩, 刘艳, 毛文涛, 刘尚旺, 刘国奇. 信息共享模型和组外贪心策略的郊狼优化算法 Coyote Optimization Algorithm Based on Information Sharing and Static Greed Selection 计算机科学, 2020, 47(5): 217-224. https://doi.org/10.11896/jsjkx.190400039 |
[2] | 杨婷, 罗飞, 丁炜超, 卢海峰. 一种自适应优化松弛量的装箱算法 Bin Packing Algorithm Based on Adaptive Optimization of Slack 计算机科学, 2020, 47(4): 211-216. https://doi.org/10.11896/jsjkx.190500132 |
[3] | 黄光球,陆秋琴. 保护区种群迁移动力学优化算法 Protected Zone-based Population Migration Dynamics Optimization Algorithm 计算机科学, 2020, 47(2): 186-194. https://doi.org/10.11896/jsjkx.181202338 |
[4] | 黄光球,赵魏娟,陆秋琴. 基于3种群Lotka-Volterra模型的种群动力学函数优化算法 Population Dynamics Optimization Based on 3Populations Lotka-Volterra Model 计算机科学, 2013, 40(8): 214-219. |
[5] | 黄光球,李涛,陆秋琴. 种群动力学优化算法 Population Dynamics-based Optimization 计算机科学, 2013, 40(11): 280-286. |
[6] | 徐宜桂 史铁林. BP网络的全局最优学习算法 计算机科学, 1996, 23(1): 73-75. |
|