一种基于校正因子的自适应简化粒子群优化算法
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摘要
针对已有粒子群算法中全局搜索和局部搜索存在盲目性和滞后性以及粒子的早熟收敛等问题,提出了一种基于校正因子的自适应简化粒子群优化算法。该算法在简化粒子群算法基础上,以粒子间平均粒距大小作为触发条件,对惯性权重、平均个体极值和全局极值进行自适应扰动。校正因子可以根据当前粒子群个体信息和全局信息自适应调整,从而完成对当前粒子状态及时准确的更新,最终使粒子可以准确而快速的找到全局最优解。对3种典型测试函数的测试结果表明该算法具有较高的全局和局部搜索能力、能够有效地避免算法陷入局部极值,是一种实用且高效的粒子群改进算法。
To overcome the problems of blindness and hysteresis during the global and local search, as well as the premature convergence shortcoming, which are in the pre-existing particle swarm optimizer algorithm, an adaptive simplified particle swarm optimization algorithm based on the correction factor is put forward in this paper. The proposed algorithm based on the simplied particle swarm optimization algorithm regards average-distance-amongst-points as the trigger condition and does the adjustment to inertia weight,the average individual extremum and global extremum.The correction factor can adapt itself according to the personal and global information of presennt particle swarm, thus updates the present particle timely and accuratly so that it can help the particles find the golbal optimal solution quickly. The experiments results of three typical testing function present that this new algorithm owns high global and local search ability and is able to effectively avoid particles trapped into local optimal solution. In conclusion, it's a practical and effective improved partical swarm algorithm.
To overcome the problems of blindness and hysteresis during the global and local search, as well as the premature convergence shortcoming, which are in the pre-existing particle swarm optimizer algorithm, an adaptive simplified particle swarm optimization algorithm based on the correction factor is put forward in this paper. The proposed algorithm based on the simplied particle swarm optimization algorithm regards average-distance-amongst-points as the trigger condition and does the adjustment to inertia weight,the average individual extremum and global extremum.The correction factor can adapt itself according to the personal and global information of presennt particle swarm, thus updates the present particle timely and accuratly so that it can help the particles find the golbal optimal solution quickly. The experiments results of three typical testing function present that this new algorithm owns high global and local search ability and is able to effectively avoid particles trapped into local optimal solution. In conclusion, it's a practical and effective improved partical swarm algorithm.
引文
[1]Kennedy J,Eberhart R.Particle swarm optimization[C]//Proceedings IEEE International Conference on Neural Networks,1995,4:1942-1948.
[2]Huang H,Qin H,Hao Z,et al..Example-based learning particle swarm optimization for continuous optimization[J].Information Sciences,2012,182(1):125-138.
[3]Mohandes M A.Modeling global solar radiation using Particle Swarm Optimization(PSO)[J].Solar Energy,2012,86(11):3137-3145.
[4]Chiou J S,Tsai S H,Liu M T.A PSO-based adaptive fuzzy PIDcontrollers[J].Simulation Modelling Practice and Theory,2012,26:49-59.
[5]Shi Y,Eberhart R.A modified particle swarm optimizer[C]//1998 IEEE International Conference on Evolutionary Computation Proceedings,IEEE World Congress on Computational Intelligence,Anchorage,AK,1998:69-73.
[6]郭鲁彦.非线性动态调整惯性权重的粒子群算法[D].沈阳:东北大学,2008:26-32.
[7]Alfi A.PSO with adaptive mutation and inertia weight and its application in parameter estimation of dynamic systems[J].Acta Automatica Sinica,2011,37(5):541-549.
[8]Lu H,Sriyanyong P,Song Y H,et al..Experimental study of a new hybrid PSO with mutation for economic dispatch with nonsmooth cost function[J].International Journal of Electrical Power&Energy Systems,2010,32(9):921-935.
[9]Wang H,Sun H,Li C,et al..Diversity enhanced particle swarm optimization with neighborhood search[J].Information Sciences,2012.
[10]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,2007,18(4):861-868.
[11]杨光友,陈定方,周国柱.粒子个体最有位置变异的粒子群优化算法[J].哈尔滨工程大学学报,2006,27(z1):531-536.
[12]Clerc M,KenneDy J.The particle swarm:Explosion stability and convergence in a multi-dimensional complex space[J].IEEE Transactions on Evolution Computer,2002,6(1):58-73.
[13]吕振肃,侯志荣.自适应变异的粒子群优化算法[J].电子学报,2004,32(3):416-420.
[14]俞欢军,张丽平,陈德钊,等.基于反馈策略的自适应粒子群优化算法[J].浙江大学学报(工学版),2005,39(9):1286-1291.
[2]Huang H,Qin H,Hao Z,et al..Example-based learning particle swarm optimization for continuous optimization[J].Information Sciences,2012,182(1):125-138.
[3]Mohandes M A.Modeling global solar radiation using Particle Swarm Optimization(PSO)[J].Solar Energy,2012,86(11):3137-3145.
[4]Chiou J S,Tsai S H,Liu M T.A PSO-based adaptive fuzzy PIDcontrollers[J].Simulation Modelling Practice and Theory,2012,26:49-59.
[5]Shi Y,Eberhart R.A modified particle swarm optimizer[C]//1998 IEEE International Conference on Evolutionary Computation Proceedings,IEEE World Congress on Computational Intelligence,Anchorage,AK,1998:69-73.
[6]郭鲁彦.非线性动态调整惯性权重的粒子群算法[D].沈阳:东北大学,2008:26-32.
[7]Alfi A.PSO with adaptive mutation and inertia weight and its application in parameter estimation of dynamic systems[J].Acta Automatica Sinica,2011,37(5):541-549.
[8]Lu H,Sriyanyong P,Song Y H,et al..Experimental study of a new hybrid PSO with mutation for economic dispatch with nonsmooth cost function[J].International Journal of Electrical Power&Energy Systems,2010,32(9):921-935.
[9]Wang H,Sun H,Li C,et al..Diversity enhanced particle swarm optimization with neighborhood search[J].Information Sciences,2012.
[10]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,2007,18(4):861-868.
[11]杨光友,陈定方,周国柱.粒子个体最有位置变异的粒子群优化算法[J].哈尔滨工程大学学报,2006,27(z1):531-536.
[12]Clerc M,KenneDy J.The particle swarm:Explosion stability and convergence in a multi-dimensional complex space[J].IEEE Transactions on Evolution Computer,2002,6(1):58-73.
[13]吕振肃,侯志荣.自适应变异的粒子群优化算法[J].电子学报,2004,32(3):416-420.
[14]俞欢军,张丽平,陈德钊,等.基于反馈策略的自适应粒子群优化算法[J].浙江大学学报(工学版),2005,39(9):1286-1291.