【摘要】 针对车间设施布局效果不佳，导致布局后搬运成本和搬运时间较高的问题，提出一种基于改进海星搜索算法的优化方法。在种群初始化阶段引入拉丁超立方采样，提高初始种群的多样性；引入Levy飞行和布朗运动策略提高解的多样性和局部探索能力，在迭代过程中实现全局搜索与局部搜索的平衡。通过算法计算复杂度分析表明复杂度并未增加，采用CEC2017基准测试函数中的12函数与SFOA、GA、PSO、GWO、WOA五种算法进行算法性能对比实验，将求得的结果进行对比，结果表明改进后的海星搜索算法在均值与标准差在大多数情况下比其他五种算法结果更好。在最终的布局方案中搬运成本降低44.47%、搬运时间减少46.33%、目标函数值下降44.81%。该方法显著提高了布局优化效率和求解精度，具有较强的实用性和可行性。更多还原

【Abstract】 An improved starfish search algorithm was proposed to optimize workshop facility layouts, so to address the existing problem of high handling costs and time. Latin hypercube sampling was used to diversify the initial population. Levy flight and Brownian motion strategies are introduced to enhance the diversity of solutions and local exploration capabilities, achieving a balance between global and local search during the iteration process. Algorithmic complexity analysis indicates no increase in computational burden. Performance comparisons against 5 algorithms(SFOA, GA, PSO, GWO, and WOA) with 12 benchmark functions from CEC2017 demonstrates that the improved starfish search algorithm yields superior mean and standard deviation results over the other 5 algorithms in most cases. It reduces transportation costs by 44.47%, transportation time by 46.33%, and the objective function value by 44.81%. This method significantly enhances layout optimization efficiency and solution accuracy, making it practical and feasible for real-world applications.更多还原