Lévy flight PSO
Lévy flight PSO
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DOI：
10.1109/cec.2015.7257220
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发表时间：
2015-05
期刊：
影响因子：
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通讯作者：
Yosuke Hariya;Takuya Kurihara;T. Shindo;K. Jin'no
中科院分区：
文献类型：
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作者：
Yosuke Hariya;Takuya Kurihara;T. Shindo;K. Jin'no
粒子群优化算法(简称PSO)。粒子群算法是一种元启发式算法，它模拟鱼群和鸟群的群体智能行为。由于粒子群算法是一种简单的算法，所以很容易实现。另外，粒子群算法不需要目标函数的梯度。因此，粒子群算法可以应用于各种优化应用。粒子群算法有两个重要的控制参数：惯性系数和加速度参数。特别是，惯性系数控制着收敛特性。为了提高解的搜索能力，提出了各种惯性系数的控制方法。在本文中，我们提出了一种新的粒子群算法，将LéVy飞行应用于惯性系数。这种新颖的粒子群算法被命名为Lévy Flight PSO(简称：Le PSO)。Lévy-PSO)。为了验证LéVY-PSO算法的性能，我们利用著名的基准函数进行了数值仿真。数值模拟结果表明，LéVy分布的重尾特性对提高LéVy-PSO算法的搜索性能具有重要意义。
The particle swarm optimization (abbr. PSO) is classified into one of meta-heuristics, it mimics the behavior of swarm intelligence of school of fish and flock of birds. Since the PSO is a simple algorithm, the implementation is easy. Also, the PSO does not require the gradient of the objective function. Therefore, the PSO can apply to various optimization applications. The PSO has two important control parameters: an inertia coefficient and an acceleration parameter. Especially, the inertia coefficient controls the convergence property. In order to improve the performance of the solution search ability, various kinds of control methods for the inertia coefficient are proposed. In this article, we propose a novel PSO that Lévy flight is applied to the inertia coefficient. The novel PSO is named Lévy flight PSO (abbr. Lévy-PSO). In order to confirm the performance of Lévy-PSO, we carry out some numerical simulations by using well-known benchmark function. The numerical simulation results indicate that the heavy-tailed of Lévy distribution is important to improve the search performance of Lévy-PSO.