摘要
萤火虫群优化算法是一种新颖的群体智能优化算法,目前在优化领域中的应用比较少。分析了萤火虫算法的仿生原理和数学模型。提出了基于萤火虫群优化算法的公差分配优化方法。以公差分配的优化目标函数作为评价函数。在基本GSO算法的基础上,使用罚函数,把它加到目标函数中,不断地寻找更优可行解,逐渐达到搜索全局最优解。通过一个具体实例测试,并将萤火虫算法和微粒群算法相比较,仿真实验结果表明萤火虫群优化算法在公差分配优化问题中有效可行,且具有较高的计算效率。
Glowworm Swarm Optimization （GSO）is a new swarm intelligence optimization algorithm,but now it has few application in the field of optimization.The bionic principle and mathematical model are analyzed.An tolerance allocation optimization method based on Glowworm Swarm Optimization algorithm is presented.Evaluation function uses optimal object function for tolerance allocation.Based on GSO,the objective function continuously finds better feasible solutions,gradually leading the search near the true optimum solution through using penalty function.Compared to PSO,the simulation results for an example indicate that the Glowworm Swarm Optimization algorithm has better feasibility,validity and efficiency for tolerance allocation optimization problem.
出处
《机械设计与制造》
北大核心
2014年第6期249-251,共3页
Machinery Design & Manufacture
基金
江西省科技厅科技支撑项目(2009ZDG08400)
关键词
萤火虫群优化算法
公差分配优化
微粒群算法
Glowworm Swam Optimization
Tolerance Allocation Optimization
Particle Swarm Optimization Algorithm
