一种改进的多策略鸽类优化算法

H. Liao, Huadong Huang
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引用次数: 0

摘要

鸽子型优化算法在求解非线性优化问题时容易陷入局部优化,且收敛精度低。本文提出了一种改进的启发鸽优化算法——高斯混合启发鸽优化算法(GPIO)。在GPIO算法中,采用混沌映射的三次映射方法对鸽子种群进行初始化,增加了种群的多样性。引入高斯变异算子,改变了鸽群算法容易陷入局部最优的缺点,提高了算法的收敛效率。19个基准函数的实验结果表明,该算法比其他群智能算法具有更好的优化能力。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
A multi strategy improved pigeon-inspired optimization algorithm
Pigeon-inspired optimization algorithm is easy to fall into local optimization and low convergence accuracy in solving nonlinear optimization problems. In this paper, an improved pigeon-inspired optimization algorithm called Gaussian mixture pigeon-inspired optimization algorithm (GPIO) is proposed. In GPIO, the cubic mapping of chaotic mapping method is used to initialize the pigeon population, which increases the diversity of the population. Gaussian mutation operator is introduced to change the shortage that pigeon swarm algorithm is easy to fall into local optimization, and improve the convergence efficiency of the algorithm. The experimental results of 19 benchmark functions show that the algorithm has better optimization ability than other swarm intelligence algorithms.
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