An Adaptive Distributed Size Wolf Pack Optimization Algorithm Using Strategy of Jumping for Raid(September 2018)

作者：Wang, Dongxing; Qian, Xu^*; Liu, Kang; Ban, Xiaojuan; Guan, Xinyu

来源：IEEE Access, 2018, 6: 65260-65274.

DOI：10.1109/ACCESS.2018.2878520

• Computational biology
• wolf pack optimization
• adaptive convergence
• distributed size
• jumping for raid

On the premise of guaranteeing the accuracy of the solution, to reduce the time spent for optimization by an existing algorithm named adaptive shrinking grid search chaos wolf optimization algorithm (ASGS-CWOA), an adaptive distribution size (ADS) wolf pack optimization algorithm using the strategy of jumping for raid (for short: WDX-WPOA) was proposed. First, the strategy of jumping for raid is proposed to improve the performance in raiding process by airlifting directly half of wolves to be around the leader wolf that can accelerate the convergence of the existing wolf pack algorithm; Moreover, an ADS was used to distribute the half of wolves to specific locations according to its rules, so as to enhance the probability of finding the optical value. Under the same condition, the results of numerical experiments demonstrate that WDX-WPOA possesses preferable optimization ability including best global search accuracy, better robustness, and fast convergence speed compared with classical genetic algorithm, particle swarm optimization algorithm, LWPS, and CWOA; especially, WDX-WPOA has better performance in time than ASGS-CWOA with the prerequisite of the same solution quality.

• 出版日期2018
• 单位清华大学; 中国矿业大学（北京）; 北京科技大学