Self-organizing hierarchical particle swarm optimizer with time-varying acceleration coefficients
Self-organizing hierarchical particle swarm optimizer with time-varying acceleration coefficients
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DOI：
10.1109/tevc.2004.826071
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发表时间：
2004-06-01
影响因子：
14.3
通讯作者：
Watson, HC
中科院分区：
文献类型：
--
作者：
Ratnaweera, A;Halgamuge, SK;Watson, HC
本文介绍了粒子群算法的一种新颖的参数自动化策略以及两个进一步的扩展，以在预定义的代数后提高其性能。最初，为了有效地控制局部搜索和收敛到全局最优解，除了粒子群优化（PSO）中的时变惯性权重因子之外，还引入了时变加速系数（TVAC）。在TVAC的基础上，讨论了两种新的策略来提高PSO的性能。首先，将“突变”的概念与TVAC（MPSO-TVAC）一起引入粒子群优化中，通过预定义的概率向随机粒子的速度矢量的随机选择的模量添加小扰动。其次，我们介绍了一种新颖的粒子群概念“具有 TVAC 的自组织分层粒子群优化器（HPSO-TVAC）”。在这种方法下，仅考虑粒子群策略的“社会”部分和“认知”部分来估计每个粒子的新速度，并且每当粒子在搜索空间中停滞时就重新初始化。此外，为了克服针对不同问题选择合适的突变步长的困难，引入了时变突变步长。此外，对于大多数基准测试，突变概率对 MPSO-TVAC 方法的性能不敏感。另一方面，还观察了重新初始化速度对 HPSO-TVAC 方法性能的影响。时变重新初始化步长被发现是 HPSO-TVAC 方法的有效参数优化策略。对于大多数功能，HPSO-TVAC 策略的性能优于本次调查中考虑的所有方法。此外，还观察到当加速系数固定为 2 时，MPSO 和 HPSO 策略的性能都很差。
This paper introduces a novel parameter automation strategy for the particle swarm algorithm and two further extensions to improve its performance after a predefined number of generations. Initially, to efficiently control the local search and convergence to the global optimum solution, time-varying acceleration coefficients (TVAC) are introduced in addition to the time-varying inertia weight factor in particle swarm optimization (PSO). From the basis of TVAC, two new strategies are discussed to improve the performance of the PSO. First, the concept of "mutation" is introduced to the particle swarm optimization along with TVAC (MPSO-TVAC), by adding a small perturbation to a randomly selected modulus of the velocity vector of a random particle by predefined probability. Second, we introduce a novel particle swarm concept "self-organizing hierarchical particle swarm optimizer with TVAC (HPSO-TVAC)." Under this method, only the "social" part and the "cognitive" part of the particle swarm strategy are considered to estimate the new velocity of each particle and particles are reinitialized whenever they are stagnated in the search space. In addition, to overcome the difficulties of selecting an appropriate mutation step size for different problems, a time-varying mutation step size was introduced. Further, for most of the benchmarks' mutation probability is found to be insensitive to the performance of MPSO-TVAC method. On the other hand, the effect of reinitialization velocity on the performance of HPSO-TVAC method is also observed. Time-varying reinitialization step size is found to be an efficient parameter optimization strategy for HPSO-TVAC method. The HPSO-TVAC strategy outperformed all the methods considered in this investigation for most of the functions. Furthermore, it has also been observed that both the MPSO and HPSO strategies perform poorly when the acceleration coefficients are fixed at two.