融合正弦余弦和变异选择的蝗虫优化算法

针对蝗虫优化算法(GOA)全局寻优能力不足,易陷入局部最优、寻优精度较低等问题,提出融合正弦余弦和变异选择的蝗虫优化算法(SC-MGOA).首先,在位置更新处根据转换概率选择不同的位置更新方式来增加种群的多样性,同时弥补GOA算法全局搜索能力不足的缺陷;其次,为更好的协调算法的全局探索和局部开发,对引入的正弦余弦机制进行改进;最后,在一定概率下针对最优解进行变异,并利用贪婪法则择优保留,使算法能够跳出局部最优,提高算法的收敛精度.选取10个测试函数进行3组测试,结果表明了不同改进策略的有效性,还证明了SC-MGOA算法相对于其他比较算法在寻优精度、寻优速度和鲁棒性等方面的优越性.

Fusion Sine Cosine and Mutation Selection Grasshopper Optimization Algorithm

In order to improve the grasshopper optimization algorithm(GOA),which was insufficient for the ability of global search,and easy to fall into local optimum and the convergence speed was slow,a grasshopper optimization algorithm combining sine cosine and mutation selection(SC-MGOA) was proposed.Firstly,the location update method is selected according to the transition probability to increase the diversity of the population,and at the same time make up for the defect of the global search ability of the GOA algorithm.Secondly,in order to better balance the global search and local search ability of the algorithm,the introduced sine cosine mechanism is improved.Finally,the new solution generated by the mutation is accepted under a certain probability,and the greedy law is used to select and reserve,so that the algorithm avoids falling into local optimum and improves the convergence precision of the algorithm.Ten test functions are selected for three sets of tests.The results show the effectiveness of different improved strategies.It also proves the superiority of SC-MGOA algorithm in terms of optimization accuracy,optimization speed and robustness compared with other comparison algorithms.