作者:李建伟，于广滨
Authors:LI Jian-wei,YU Guang-bin
摘要:摘要:针对麻雀搜索算法（SSA）存在开发能力较差，在接近全局最优时，种群多样性减少，易陷入局部最优解等问题，提出一种改进的麻雀搜索算法（ISSA）。该算法引进粒子共生机制、Levy飞行机制，利用当前个体来平衡个体只由历史最优个体和全局最优个体引导三种策略，和其他4个算法对12个基准函数进行仿真实验，实验表明ISSA 提高了全局最优搜索能力、避免算法陷入局部最优，基于体积和效率建立轮毂减速器多目标优化模型，优化后减速器在保证效率不变的情况下体积降低53.44%。
Abstract:Abstract: An improved sparrow search algorithm (ISSA) is proposed to solve the problems of poor development ability of sparrow search algorithm (SSA)， reduced population diversity when approaching the global optimum， and easy to fall into the local optimum solution. The algorithm introduces the particle symbiosis mechanism and Levy flight mechanism， uses the current individual to balance the individual， and is guided only by the historical optimal individual and the global optimal individual. The simulation experiments with other four algorithms on 12 benchmark functions show that Issa improves the global optimal search ability and avoids the algorithm from falling into local optimization. Based on volume and efficiency， a multi-objective optimization model of wheel reducer is established. After optimization， the volume of the reducer is reduced by 53.44% with the same efficiency.


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