摘要 在继承综合学习粒子群算法（Comprehensive Learning Particle Swarm Optimizer，CLPSO）全局探索优势的基础上，引入具有高效收敛性能的传统局部搜索（Orthodox Local Search，OLS）方法，提出了基于拟熵自适应启动局部搜索策略的混合粒子群算法（Hybrid Particle Swarm Optimization algorithm with Adaptive starting strategy of Local Search based on Quasi-Entropy，ALSQE-HPSO）.采用拟熵指标解决何时启动OLS这一关键问题.对8个标准函数的10维和20维问题的测试结果，表明了ALSQE-HPSO算法的性能优势.本文提出的算法也与包含两种基于CLPSO的改进算法和一种带OLS的粒子群算法在内的其他6种改进粒子群算法进行了对比，实验结果表明ALSQE-HPSO算法的性能优于对比算法.
Abstract：Based on inheriting the advantage of global exploration of Comprehensive Learning Particle Swarm Optimizer (CLPSO),the Orthodox Local Search (OLS) approaches with efficient convergence are introduced and a Hybrid Particle Swarm Optimization algorithm with Adaptive starting strategy of Local Search based on Quasi-Entropy (ALSQE-HPSO) is proposed.A quasi-entropy index is utilized to solve the key issue of when to start OLS.The test results of 10-dimension and 20-dimension of eight benchmark functions show the performance advantages of the ALSQE-HPSO algorithm.The comparisons between the proposed algorithm and six other improved PSO algorithms,including two improved CLPSO algorithms and one PSO algorithm with OLS,are also made.The numerical results indicate that the performance of the ALSQE-HPSO is superior to the compared algorithms.
曹玉莲, 李文锋, 张煜. 基于拟熵自适应启动局部搜索策略的混合粒子群算法[J]. 电子学报, 2018, 46(1): 110-117.
CAO Yu-lian, LI Wen-feng, ZHANG Yu. Hybrid Particle Swarm Optimization Algorithm with Adaptive Starting Strategy of Local Search Based on Quasi-Entropy. Acta Electronica Sinica, 2018, 46(1): 110-117.
[1] KENNEDY J,EBERHART R C.Particle swarm optimization[A].Proceedings of IEEE International Conference on Neural Networks[C]. Perth:IEEE,1995.1942-1948.
[2] 丁旭,吴晓蓓,黄成.基于改进粒子群算法和特征点集的无线传感器网络覆盖问题研究[J].电子学报,2016,44(4):967-973. DING Xu,WU Xiao-bei,HUANG Cheng.Area coverage problem based on improved PSO algorithm and feature point set in wireless sensor networks[J].Acta Electronica Sinica,2016,44(4):967-973.(in Chinese)
[3] XIONG T,BAO Y,HU Z,et al.Forecasting interval time series using a fully complex-valued RBF neural network with DPSO and PSO algorithms[J].Information Sciences,2015,305:77-92.
[4] 李文锋,梁晓磊,张煜.具有异构分簇的粒子群优化算法研究[J].电子学报,2012,40(11):2194-2199. LI Wen-feng,LIANG Xiao-lei,ZHANG Yu.Research on PSO with clusters and heterogeneity[J].Acta Electronica Sinica,2012,40(11):2194-2199.(in Chinese)
[5] TANWEER M R,SURESH S,SUNDARARAJAN N.Dynamic mentoring and self-regulation based particle swarm optimization algorithm for solving complex real-world optimization problems[J].Information Sciences,2016,326:1-24.
[6] 邵鹏,吴志健,周炫余,等.基于折射原理反向学习模型的改进粒子群算法[J].电子学报,2015,43(11):2137-2144. SHAO Peng,WU Zhi-jian,ZHOU Xuan-yu,et al.Improved particle swarm optimization algorithm based on opposite learning of refraction[J].Acta Electronica Sinica,2015,43(11):2137-2144.(in Chinese)
[7] 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.
[8] NASIR M,DAS S,MAITY D,et al.A dynamic neighborhood learning based particle swarm optimizer for global numerical optimization[J].Information Sciences,2012,209:16-36.
[9] LYNN N,SUGANTHAN P N.Heterogeneous comprehensive learning particle swarm optimization with enhanced exploration and exploitation[J].Swarm and Evolutionary Computation,2015,24:11-24.
[10] LUENBERGER DG,YE Y.Linear and Nonlinear Programming (Fourth Edition)[M].Switzerland:Springer International Publishing,2015.
[11] PABLO M.On evolution,search,optimization,genetic algorithms and martial arts:towards memetic algorithms[A].Caltech Concurrent Computation Program,Technique Report[R].USA,1989.158-179.
[12] ZHAO S Z,LIANG J J,SUGANTHAN P N,et al.Dynamic multi-swarm particle swarm optimizer with local search for large scale global optimization[A].IEEE Congress on Evolutionary Computation[C].Hong Kong,China:IEEE,2008.3845-3852.
[13] HAN F,LIU Q.An improved hybrid PSO based on ARPSO and the quasi-newton method[A].International Conference in Swarm Intelligence[C].Cham:Springer International Publishing,2015.460-467.
[14] TRELEA I C.The particle swarm optimization algorithm:convergence analysis and parameter selection[J].Information Processing Letters,2003,85(6):317-325.
[15] BROYDEN C G,DENNIS J E,MORé J J.On the local and superlinear convergence of quasi-newton methods[J].Journal of the Institute of Mathematics & Its Applications,1973,12(3):223-245.
[16] PETALAS Y G,PARSOPOULOS K E,VRAHATIS M N.Memetic particle swarm optimization[J].Annals of Operations Research,2007,156(1):99-127.
[17] PARSOPOULOS KE,VRAHATIS MN.Unified particle swarm optimization in dynamic environments.Applications of evolutionary computing[A].FRANZ R.Proceedings of EvoWorkshops[C].Berlin Heidelberg:Springer,2005.590-599.
[18] PERAM T,VEERAMACHANENI K,Mohan CK.Fitness-distance-ratio based particle swarm optimization[A].Proceedings of IEEE Swarm Intelligence Symposium[C].Indianapolis:IEEE,2003.174-181.
[19] TANWEER M R,SURESH S,SUNDARARAJAN N.Self regulating particle swarm optimization algorithm[J].Information Sciences,2015,294:182-202.
2018, Vol. 46 
