Beijing Advanced Innovation Center for Materials Genome Engineering, University of Science &Technology Beijing, Beijing 100083, China.
School of Mechanical Electronic & Information Engineering, China University of Mining & Technology-Beijing, Beijing 100083, China.
Comput Intell Neurosci. 2020 May 18;2020:7986982. doi: 10.1155/2020/7986982. eCollection 2020.
To improve the optimization quality, stability, and speed of convergence of wolf pack algorithm, an adaptive shrinking grid search chaotic wolf optimization algorithm using standard deviation updating amount (ASGS-CWOA) was proposed. First of all, a strategy of adaptive shrinking grid search (ASGS) was designed for wolf pack algorithm to enhance its searching capability through which all wolves in the pack are allowed to compete as the leader wolf in order to improve the probability of finding the global optimization. Furthermore, opposite-middle raid method (OMR) is used in the wolf pack algorithm to accelerate its convergence rate. Finally, "Standard Deviation Updating Amount" (SDUA) is adopted for the process of population regeneration, aimed at enhancing biodiversity of the population. The experimental results indicate that compared with traditional genetic algorithm (GA), particle swarm optimization (PSO), leading wolf pack algorithm (LWPS), and chaos wolf optimization algorithm (CWOA), ASGS-CWOA has a faster convergence speed, better global search accuracy, and high robustness under the same conditions.
为了提高狼群算法的优化质量、稳定性和收敛速度，提出了一种基于标准差更新量的自适应收缩网格搜索混沌狼群优化算法（ASGS-CWOA）。首先，为狼群算法设计了一种自适应收缩网格搜索策略（ASGS），通过该策略，允许狼群中的所有狼竞争成为头狼，以提高发现全局最优解的概率，从而增强其搜索能力。此外，在狼群算法中采用了对中突袭法（OMR），以加快其收敛速度。最后，在种群再生过程中采用了“标准差更新量”（SDUA），以增强种群的生物多样性。实验结果表明，与传统遗传算法（GA）、粒子群优化算法（PSO）、领头狼狼群算法（LWPS）和混沌狼群优化算法（CWOA）相比，在相同条件下，ASGS-CWOA 具有更快的收敛速度、更好的全局搜索精度和更高的鲁棒性。
