生态金字塔粒子群优化算法

doi:10.11896/j.issn.1002-137X.2017.10.043

摘要/Abstract

摘要： 为解决粒子群优化算法在处理高维复杂函数时容易陷入局部最优和早熟收敛的问题,提出生态金字塔粒子群优化算法(EP-PSO)。该算法引入生态金字塔系统,使粒子在搜索空间分等级、分子群寻优,有效增加了群体多样性；为增强算法的全局搜索能力,对处于停滞状态的个体极值和全局极值进行动态变异,以达到扩大种群潜在搜索空间的效果。选择15个测试函数验证算法的有效性,结果表明EP-PSO有着良好的寻优性能,能够得到较高精度解,具有较高的效率和可信度。

关键词: 粒子群优化算法,早熟收敛,生态金字塔系统

Abstract: A novel ecological pyramid particle swarm optimization variant was proposed to deal with the high dimensions,complex optimization problems.In the new variant,the ecological pyramid system was introduced to improve the particle’s diversity.At the same time,the variation both on the local exemplar and the global exemplar was also employed extending the search space.To verify the effectiveness of the algorithm,fifteen benchmark problems were used to test the performance of EP-PSO.Experimental results validate the outstanding performance of EP-PSO.Compared with other algorithms,EP-PSO not only obtained high accuracy solutions,but also achieved high efficiency and reliability.

Key words: Particle swarm optimization algorithm,Premature convergence,Ecological pyramid system

刘亚红,张玮,樊吕彬. 生态金字塔粒子群优化算法[J]. 计算机科学, 2017, 44(10): 237-244. https://doi.org/10.11896/j.issn.1002-137X.2017.10.043

LIU Ya-hong, ZHANG Wei and FAN Lv-bin. Ecological Pyramid Particle Swarm Optimization[J]. Computer Science, 2017, 44(10): 237-244. https://doi.org/10.11896/j.issn.1002-137X.2017.10.043

参考文献

[1] KENNEDY J.Particle Swarm Optimization[C]∥Proc of IEEE International Conference on Neural Networks.1995:1942-1948.
[2] LIU J,MA D,ZHANG W,et al.Dynamic Multi-Species Particle Swarm Optimization Based on Food Chain Model[J].Journal of Computer Application,2016,6(5):1341-1346.(in Chinese) 刘角,马迪,张玮,等.基于食物链机制的动态多物种粒子群算法[J].计算机应用,2016,6(5):1341-1346.
[3] ZHANG W.Research on Particle Swarm Optimization Algo-rithm and its Application in Array Antenna[D].Taiyuan:Taiyuan University of Technology,2010.(in Chinese) 张玮.粒子群优化算法研究及在阵列天线中的应用[D].太原:太原理工大学,2010.
[4] SHI Y,EBERHART R.A modified particle swarm optimizer[C]∥ Proceedings of the 1998 IEEE World Congress on Computational Intelligence.Piscataway,NJ:IEEE,1998:69-73.
[5] ZHANG W,LIU J,FAN L B,et al.Control Strategy PSO[J].Applied Soft Computing,2016,8(C):75-86.
[6] 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.
[7] LIM W H,MATISA N A.Teaching and peer-learning particle swarm optimization[J].Applied Soft Computing,2014,8(18):39-58.
[8] ZHANG Y J,LI L,ZHANG Y H,et al.An Improved Particle Swarm Optimization Algorithm Based on Two subpopulation[J].Journal of Hunan University (Natural Sciences),2011,38(1):84-88.(in Chinese) 张英杰,李亮,张英豪,等.一种基于双子群的改进粒子群优化算法[J].湖南大学学报(自然科学版),2011,8(1):84-88.
[9] WANG W B,FENG Q Y.Chaotic particle swarm optimization algorithm based on hierarchical multi-subpopulation[J].Control and Decision, 2010,5(11):1663-1668.(in Chinese) 王维博,冯全源.基于分层多子群的混沌粒子群优化算法[J].控制与决策,2010,5(11):1663-1668.
[10] ZHAO X C,LIU G L,LIU H Q,et al.Particle swarm optimization based on non-uniform mutation and multiple stages perturbation[J].Chinese Journal of Computers,2014,7(9):2058-2070.(in Chinese) 赵新超,刘国莅,刘虎球,等.基于非均匀变异和多阶段扰动的粒子群优化算法[J].计算机学报,2014,7(9):2058-2070.
[11] ZHAO Y D,FANG Z H.Pariticle swarm optimization algorithm with weight function’s learning factor[J].Journal of Computer Application,2013,3(8):2265-2268.(in Chinese) 赵远东,方正华.带有惯性权重函数学习因子的粒子群算法[J].计算机应用,2013,3(8):2265-2268.
[12] LINDEMAN R L.The trophic-dynamic aspect of ecology[J].Ecology,1942,3(10):399-418.
[13] HU W,LI Z S.A simpler and More Effective Particle Swarm Optimization[J].Jounal of Software,2007,8(4):861-868.(in Chinese) 胡旺,李志蜀.一种更简化而高效的粒子群优化算法[J].软件学报,2007,8(4):861-868.
[14] ZOU D X,WANG G N,GAO L Q,et al.Application of Improved PSO Algorithm to Reliability Problems[J].Journal of Northeastern University(Natural Science),2010,1(9):1234-1237.(in Chinese) 邹德璇,王高楠,高立群,等.改进的粒子群优化算法在可靠性问题中的应用[J].东北大学学报(自然科学版),2010,1(9):1234-1237.
[15] SUGANTHAN P N,HANSEN N,LIANG J J,et al.Problem definitions and evaluation criteria for the CEC 2005 special session on real-parameter optimization[R].Singapore,Nanyang Technological University,2005.
[16] 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,2006,0(3):281-295.
[17] ZHAN Z H,ZHANG J,LI Y,et al.Adaptive particle swarm optimization[J].IEEE Transactions on Systems,Man,and Cybernetics,2009,9(6):1362-1381.
[18] CLERC M,KENNEDY J.The particle swarm explosion,stability,and convergence in a multidimensional complex space[J].IEEE Transactions on Evolutionary Computation,2002,6(1):58-73.
[19] TANG Y,WANG Z,FANG J A.Feedback learning particleswarm optimization[J].Applied Soft Computing,2011,1(8):4713-4725.
[20] DE OCA M A M,STUTZLE T,BIRATTARI M,et al.Frankenstein’s PSO:acomposite particle swarm optimization algorithm[J].IEEE Transactions on Evolutionary Computation,2009,3(5):1120-1132.
[21] MeENDES R,KENNEDY J,NEVES J.The fully informed particle swarm:simpler,maybe better[J].IEEE Transactions on Evolutionary Computation,2004,8(3):204-210.
[22] RATNAWEERA A,HALGAMUGE S K,W ATSON H C.Self-organizing hierarchical particle swarm optimizer with time-varying acceleration coefficients[J].IEEE Transactions on Evolutionary Computation,2004,8(3):240-255.
[23] ZHOU D,GAO X,LIU G,et al.Randomization in particleswarm optimization for global search ability[J].Expert Systems with Applications,2013,8(12):15356-15364.

Metrics

Viewed

Full text

Abstract

Cited

Shared

Discussed