求解大规模优化问题的改进麻雀搜索算法

针对麻雀搜索算法在求解大规模优化问题时存在收敛速度慢、寻优精度低和易陷入局部极值的缺点,提出一种基于精英反向学习策略的萤火虫麻雀搜索算法(ELFASSA).首先,通过反向学习策略初始化种群,为全局寻优奠定基础;其次,利用萤火虫扰动策略提高算法跳出局部最优的能力并加速收敛;最后,在麻雀位置更新后引入精英反向学习策略以获取精英解及动态边界,使精英反向解可以定位在狭窄的搜索空间中,有利于算法收敛.通过选取10个高维标准测试函数进行仿真实验,将其与麻雀搜索算法(SSA)及4种先进的改进算法进行性能对比,并与3种单一策略改进的麻雀搜索算法进行改进策略的有效性分析,仿真结果表明, ELFASSA算法在收敛速度和求解精度两方面明显优于其他对比算法.

An improved sparrow search algorithm for solving large-scale optimization problems

Aiming at the disadvantages of slow convergence, low optimization accuracy and easy to fall into local extremum in the sparrow search algorithm for solving large-scale optimization problems, sparrow search algorithm based on the elite reverse learning strategy and firefly strategy(ELFASSA) is proposed. Firstly, the population is initialized using the reverse learning strategy to lay the foundation for global optimization. Then, the firefly perturbation strategy is used to improve the ability of the algorithm to jump out of the local optimum and accelerate the convergence. Finally, after the sparrow position is updated, the elite reverse learning strategy is introduced to obtain the elite solution and dynamic boundary, so that the elite reverse solution can be located in the narrow search space, which is conducive to the convergence of the algorithm. By selecting 10 high-dimensional standard test functions for simulation experiments, its performance is compared with the sparrow search algorithm (SSA) and four advanced improved algorithms, and the effectiveness of the improved strategy is analyzed with three single strategy improved sparrow search algorithms. The simulation results show that the ELFASSA algorithm is obviously superior to other comparison algorithms in convergence speed and solution accuracy.