基于正交对立学习的改进麻雀搜索算法

针对麻雀搜索算法种群多样性少,局部搜索能力弱的问题,本文提出了基于正交对立学习的改进型麻雀搜索算法(OOLSSA)。首先,在算法中引入正态变异算子,丰富算法种群多样性;其次,利用对立学习策略,增强算法跳出局部最优的能力;然后,在加入者更新之后引入正交对立学习机制,加快算法的收敛速度;最后,基于15个基准测试函数与6个传统优化算法和2个改进型算法进行仿真实验、非参数Friedman检验以及算法平衡能力进行分析,评估OOLSSA算法寻优性能。仿真结果证明,OOLSSA与其余8种算法相比,算法的探索开发能力以及收敛速度都表现良好。

Improved sparrow search algorithm based on orthogonal-opposition-based learning

To solve the problem of low population diversity and weak exploitation of sparrow search algorithm, an improved sparrow search algorithm based on orthogonal-opposition-based learning (OOLSSA) is proposed in this paper. First, a normal mutation operator is used to enrich the diversity of algorithm population. Second, the opposition-based learning is used to enhance the ability of the algorithm to jump out of local optimum. Then, orthogonal-opposition-based learning is introduced after the update of the scrounger position to accelerate the convergence of the algorithm. Finally, performance test based on fifteen benchmark test functions, non-parametric Friedman test and balance analysis of algorithms shows that compared with six traditional optimization algorithms and two improved algorithms, OOLSSA has better searching performance on exploration and exploitation ability and convergence speed.