一种多种群综合学习粒子群优化算法
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摘要
针对综合学习算法(Comprehensive learning particle swarm optimization,CLPSO)在解决全局优化问题时精度不高且收敛速度慢的问题,提出一种多种群综合学习算法(MS_CLPSO).该算法将传统粒子群算法的社会部分引入CLPSO算法,有效提高了算法的收敛速度和局部开采能力;同时,为扩大粒子的空间搜索范围,算法引入多种群策略,提高了算法全局勘探能力;并针对可能陷入局部极值的粒子,采用全局学习策略更新学习样本,增加了种群中粒子多样性.实验结果表明,在处理单峰和多峰标准测试函数中,MSCLPSO算法有效提高了CLPSO算法的精度和收敛速度.
In order to solve the problem of low precision and slow convergence of comprehensive learning particle swarm algorithm, a multiswarm comprehensive learning particle swarm optimization(MS_CLPSO) is proposed. MS-CLPSO introduced social part of standard particle swarm optimization(PSO) into Comprehensive learning particle swarm optimization(CLPSO), which enhances the convergence rate of the algorithm. Meanwhile, the MS-CLPSO uses muti-swarm strategy to improve the global exploration ability and broaden potential search area of the algorithm. To help particles trapped in local optimum, global learning strategy is adopted to update learning exemplar, and it increases the particle diversity effectively. The experiment result shows that MS-CLPSO improves the precision and convergence rate of CLPSO dramatically for unimodal and multimodal standard test functions.
In order to solve the problem of low precision and slow convergence of comprehensive learning particle swarm algorithm, a multiswarm comprehensive learning particle swarm optimization(MS_CLPSO) is proposed. MS-CLPSO introduced social part of standard particle swarm optimization(PSO) into Comprehensive learning particle swarm optimization(CLPSO), which enhances the convergence rate of the algorithm. Meanwhile, the MS-CLPSO uses muti-swarm strategy to improve the global exploration ability and broaden potential search area of the algorithm. To help particles trapped in local optimum, global learning strategy is adopted to update learning exemplar, and it increases the particle diversity effectively. The experiment result shows that MS-CLPSO improves the precision and convergence rate of CLPSO dramatically for unimodal and multimodal standard test functions.
引文
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[8]曲良东,何登旭.自适应柯西变异人工鱼群算法及其应用[J].微电子学与计算机,2010, 27(10):74-78.
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[10]李军军,黄有方,杨斌等.交替捕食的粒子群优化算法及其粒子轨迹收敛性分析[J].控制理论与应用,2013, 30(7):000850-855.
[11]肖健梅,李军军,王锡淮.梯度微粒群优化算法及其收敛性分析[J].控制与决策,2009, 24(4):560-564.
[12] Shi B Y, Eberhart R. A modified particle swarm optimizer, The 1998[C]//Evolutionary Computation Proceedings, 1998. IEEE World Congress on Computational Intelligence. The 1998 IEEE International Conference on. IEEE, 2010:69-73.
[13] Mendes R, Kennedy J, Neves J. The fully informed particle swarm:simpler, maybe better[J]. IEEE Transactions on Evolutionary Computation, 2004, 8(3):204-210.
[14] Ratnaweera A,Halgamuge S K,Watson H C. Self-organizing hierarchical particle swarm optimizerwith time-varying acceleration coefficients[J]. IEEE Transactions on Evolutionary Computation,2004, 8(3):240-255.
[15]张水平,王碧.动态搜索空间的粒子群算法[J].计算机应用研究,2016, 33(7):2047-2050.
[16] Zielinski K, Peters D, Laur R. Run time analysis regarding stopping criteria for differential evolution and particle swarm optimization[C]//2005.
[2]杜贞,叶春明,凌远雄.应用萤火虫算法求解基于学习效应的PFSP问题[J].计算机工程与应用,2015,51(16):248-251.
[3]宋玉坚,叶春明,黄佐珲.多智能体入侵杂草算法[J].计算机应用研究,2014,31(10):2957-2961.
[4] L iang J J, Qin A K, Suganthan P N, et al. Comprehensive learning particle swarm optimizer for global optimization of multimodal functions[J]. Evolutionary Computation IEEE Transactions on,2006, 10(3):281-295.
[5] Shi B Y, Eberhart R. A modified particle swarm optimizer, The 1998[C]//Evolutionary Computation Proceedings, 1998. IEEE World Congress on Computational Intelligence. The 1998 IEEE International Conference on IEEE, 2010:69-73.
[6] 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(5):16-36.
[7] Yao X, Liu Y, Lin G. Evolutionary programming made faster[J]. IEEE Transactions on Evolutionary Computation, 2002, 3(2):82-102.
[8]曲良东,何登旭.自适应柯西变异人工鱼群算法及其应用[J].微电子学与计算机,2010, 27(10):74-78.
[9]余伟伟,谢承旺.一种多策略混合的粒子群优化算法[J].计算机科学,2018(s1).
[10]李军军,黄有方,杨斌等.交替捕食的粒子群优化算法及其粒子轨迹收敛性分析[J].控制理论与应用,2013, 30(7):000850-855.
[11]肖健梅,李军军,王锡淮.梯度微粒群优化算法及其收敛性分析[J].控制与决策,2009, 24(4):560-564.
[12] Shi B Y, Eberhart R. A modified particle swarm optimizer, The 1998[C]//Evolutionary Computation Proceedings, 1998. IEEE World Congress on Computational Intelligence. The 1998 IEEE International Conference on. IEEE, 2010:69-73.
[13] Mendes R, Kennedy J, Neves J. The fully informed particle swarm:simpler, maybe better[J]. IEEE Transactions on Evolutionary Computation, 2004, 8(3):204-210.
[14] Ratnaweera A,Halgamuge S K,Watson H C. Self-organizing hierarchical particle swarm optimizerwith time-varying acceleration coefficients[J]. IEEE Transactions on Evolutionary Computation,2004, 8(3):240-255.
[15]张水平,王碧.动态搜索空间的粒子群算法[J].计算机应用研究,2016, 33(7):2047-2050.
[16] Zielinski K, Peters D, Laur R. Run time analysis regarding stopping criteria for differential evolution and particle swarm optimization[C]//2005.