A perturbed particle swarm algorithm for numerical optimization
A perturbed particle swarm algorithm for numerical optimization
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DOI：
10.1016/j.asoc.2009.06.010
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发表时间：
2010-01-01
影响因子：
8.7
通讯作者：
Zhao Xinchao
中科院分区：
文献类型：
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作者：
Zhao Xinchao
经典粒子群算法(PSO)有其自身的缺点，如收敛速度快，在优化过程中往往会迅速失去多样性，不可避免地会导致不良的早熟收敛。针对粒子群算法的不足，提出了一种基于扰动全局最优概念的扰动粒子群算法(PPSA)，以解决粒子群算法的早熟收敛和多样性维持问题。为了理解和分析扰动粒子更新策略的不确定性，给出了一个线性模型和一个随机模型以及初始的极大极小模型。使用12个标准测试函数对PPSA进行了验证。初步结果表明，粒子群优化算法在解的质量和稳健性方面都明显优于粒子群优化算法，与GCPSO算法相当。实验证明，扰动粒子更新策略是随机启发式算法的一种鼓舞人心的策略，极大极小模型是可能性度量概念的一种很有前途的模型。(C)2009爱思唯尔B.V.保留所有权利。
The canonical particle swarm optimization (PSO) has its own disadvantages, such as the high speed of convergence which often implies a rapid loss of diversity during the optimization process, which inevitably leads to undesirable premature convergence. In order to overcome the disadvantage of PSO, a perturbed particle swarm algorithm (pPSA) is presented based on the new particle updating strategy which is based upon the concept of perturbed global best to deal with the problem of premature convergence and diversity maintenance within the swarm. A linear model and a random model together with the initial max-min model are provided to understand and analyze the uncertainty of perturbed particle updating strategy. pPSA is validated using 12 standard test functions. The preliminary results indicate that pPSO performs much better than PSO both in quality of solutions and robustness and comparable with GCPSO. The experiments confirm us that the perturbed particle updating strategy is an encouraging strategy for stochastic heuristic algorithms and the max-min model is a promising model on the concept of possibility measure. (C) 2009 Elsevier B. V. All rights reserved.