多角色优化策略的灰狼-共生生物搜索算法

Multi Role Optimization Strategic Grey Wolf Optimizer-symbiotic Organisms Search Algorithm

针对共生生物搜索算法存在的易陷入局部最优及搜索停滞等缺陷,提出一种基于多角色优化策略的混合灰狼-共生生物搜索算法(MRSSOS).从算法内部结构、停止防止机制、混合智能优化算法三个方面对标准SOS算法进行改进,减少无效搜索的同时保持种群多样性,进一步平衡算法迭代过程中的探索能力与挖掘能力.实验测试结果表明,改进后的MRSSOS算法性能明显更好,选取的10个单峰函数中,9个都可在1000次迭代内收敛到理论最优解,9个多峰函数中,5个可达最优解,另外2个解优于对比算法,表明MRSSOS在收敛速度、求解精度、稳定性方面性能优势明显.

Aiming at the defects of symbiotic biology search algorithm,such as easy to fall into local optimization and search stagnation,a hybrid gray wolf symbiotic biology search algorithm(mrssos)based on multi role optimization strategy is proposed.This paper improves the standard SOS algorithm from three aspects:the internal structure of the algorithm,the stop prevention mechanism and the hybrid intelligent optimization algorithm,so as to reduce the invalid search while maintaining the diversity of the population,and further balance the exploration ability and mining ability in the iterative process of the algorithm.The experimental results show that the performance of the improved mrssos algorithm is obviously better.Nine of the 10 unimodal functions selected can converge to the theoretical optimal solution in 1000 iterations.Among the nine multimodal functions,five can reach the optimal solution,and the other two solutions are better than the comparative algorithm.It shows that mrssos has obvious performance advantages in global search ability,convergence speed,solution accuracy and robustness.