多邻域结构多目标遗传算法  

Multi-neighborhood Structure Based Multi-objective Genetic Algorithm

作　　者：朱大林[1] 詹腾[1,2] 张屹[1] 郑小东[1] 张灯皇 余竹玛 

机构地区：[1]三峡大学机械与动力学院,宜昌443002 [2]株洲联成集团有限责任公司,株洲412001

出　　处：《农业机械学报》2015年第4期309-315,324,共8页Transactions of the Chinese Society for Agricultural Machinery

基　　金：国家自然科学基金资助项目(51275274)

摘　　要：为了解决应力约束类桁架结构的尺寸优化多目标问题,提出一种多领域结构的多目标遗传算法应用于尺寸优化设计。利用个体之间欧氏距离信息,将种群划分为多个领域以形成多个小生境种群。该算法为每个个体提供一定数量的邻居个体,并规定只能同邻居个体进行交叉变异操作,通过实验分析了不同邻居规模对算法性能的影响。将新算法与其他经典算法在18个标准测试函数上进行了仿真分析,结果表明,所得到的Pareto前端分布更加均匀且更加逼近真实Pareto前端,具有良好的收敛性和多样性。将该算法应用于经典的25杆空间桁架结构优化的求解,获得Pareto前端更均匀,收敛性更好,相对于其他的优化算法具有更好的优化效果。该算法在程序设计、求解空间及其方法通用性等方面表现出良好的性能,并且简单、实用,更加适合于工程实际应用。In order to solve the problem of multi-objective size optimization of truss structures with stress constraints,a multi-objective optimization algorithm with multi-neighborhood was proposed.Based on the Euclidean distance between individuals,the population was divided into multi-neighborhood to form several niche populations.A number of individuals were assigned to each cell as neighborhood by the proposed algorithm.The individuals were only allowed interacting with each other within its neighborhood and generating offspring.The influence of different sizes of neighbors on the performance was analyzed through simulation experiments.The test results on 18 benchmarks revealed that the proposed algorithm outperformed some state-of-the-art algorithm in terms of covered area and diversity,which showed good uniformity and diversity.The obtained Pareto front showed good uniformity and diversity when solving the classic multi-objective optimization problem of 25-bar truss structure.The algorithm showed good performance in program design,solution space and generality and so on,which was very simple,practical and suitable for engineering practice.

关 键 词：多邻域结构 邻居规模 桁架结构 多目标优化 

分 类 号：TP301.6[自动化与计算机技术—计算机系统结构]