基于精英反向学习策略的麻雀搜索算法

Sparrow Search Algorithm Based on Elite Reverse Learning Strategy

针对麻雀搜索算法(Sparrow Optimization Algorithm,SSA)存在收敛速度慢、容易陷入局部极值的问题,提出了一种基于精英反向学习(EOBL,elite opposition-based learning)的麻雀搜索算法。该算法引入精英反向学习策略,通过精英个体反向学习生成反向解,同时获取精英麻雀的动态边界,对比更新前后麻雀,如果更优则替代之前的麻雀,从当前解和精英反向解中挑选优异个体作为下一代种群,有效的提高了算法全局搜索能力和搜索精度。通过标准测试函数进行对比仿真,结果表明加入精英反向学习麻雀搜索算法具有很好的全局搜索能力和收敛速度。

Aiming at the problems of the Sparrow Optimization Algorithm(SSA),which has slow convergence speed and easy to fall into local extremes,a sparrow search algorithm based on elite opposition-based learning(EOBL)is proposed.The algorithm introduces an elite reverse learning strategy,generates a reverse solution through the reverse learning of an elite individual,and obtains the dynamic boundary of the elite sparrow at the same time,compares the sparrow before and after the update,and replaces the previous sparrow if it is better,and selects excellent individuals from the current solution and the elite reverse solution as the next generation population,which effectively improves the algorithm’s global search ability and search accuracy of the algorithm.Comparing simulation experiments with standard test functions,the results show that joining the elite reverse learning sparrow search algorithm has a good global search ability and convergence speed.