一种基于灰狼策略的大白鲨混合优化算法

A White Shark Hybrid Optimization Algorithm based on the Grey Wolf Strategy

大白鲨优化算法可以有效解决一些连续搜索空间的优化问题,但存在易于陷入局部最优解和收敛速度慢等问题。提出的混合灰狼策略的大白鲨优化算法,通过结合灰狼优化算法中头狼的围剿策略,改进大白鲨优化算法中白鲨的围剿方案,可以获得更佳的局部开发能力。在大白鲨优化算法中的声音定位部分,通过引入变异的方式增加随机性,以解决局部最优问题。通过对算法进行Benchmark测试,并与粒子群、灰狼优化、白鲨优化算法进行比较,实验结果表明该算法在局部开发能力、收敛速度等方面有较好提升。

The White Shark Optimization algorithm can effectively solve some continuous search space optimization problems,but there are problems such as easy falling into the local optimal solution and slow convergence speed.The Great White Shark Optimization algorithm with a mixed Grey Wolf strategy proposed in this paper was to use the leader wolf s encirclement and suppression strategy in the Grey Wolf Optimization algorithm to improve the the White Shark s encirclement and suppression strategy in the Great White Shark Optimization algorithm.This new strategy can get better local development capabilities.In the sound localization part of the White Shark Optimization algorithm,the randomness was increased by introducing variation to solve the local optimal problem.Through the Benchmark test of the algorithm,and compared with particle swarm optimization,Gray Wolf Optimization,and White Shark Optimization algorithms,the experimental results show that the algorithm has a good improvement in local development ability,convergence speed,and other aspects.