一种改进的多目标蚁群优化算法  

An Improved Multi-Objective Ant Colony Optimization Algorithm

作　　者：黄坤[1] 吴俊[1] 

机构地区：[1]义乌工商职业技术学院,浙江义乌322000

出　　处：《微电子学与计算机》2011年第10期181-183,187,共4页Microelectronics & Computer

摘　　要：提出了一种改进的多目标优化问题的蚁群算法.算法选择进化算法的定义的时候,种群中一定数量的个体信息来源作为中心的扩散,多个中心点之间有一定的距离;群体中的其他个体按照离源个体最近的距离的原则归属于其中一个信息素扩散源;按照信息素扩散算法,每一信息素扩散源中的个体获得源于中心点的信息素;保留每一代群体中的中心点到下一代种群中,确保了收敛性和维护种群的多样性.最后利用多目标背包问题来测试算法的性能,并与MOA和NSGA-II算法进行了分析比较.结果表明,该搜索效率高,向真实Pareto前沿逼近效果好,得到传播的多种解决方案,是一个多目标优化问题的解决和有效的方法.For the characteristics of multi-objective optimization problems is proposed for multi-objective optimization problems ant colony algorithm.Definition of evolutionary algorithms selected from the population when a certain number of individual sources as the center of pheromone diffusion,more than the distance between the centers of intervals;group of other individuals in accordance with the distance from the nearest source of the principle of individual ownership in one of the Pheromone diffusion source;each spread source of pheromone pheromone diffusion algorithm in accordance with the individual to obtain from the center of the pheromone;each generation groups in the center point to the next generation population retained to ensure convergence and maintenance groups Diversity.Finally,multi-objective knapsack problem to test algorithm performance,and with the MOA and the NSGA-II algorithm was simulated compared.Results show that the search efficiency,the true Pareto front approximation to good effect,to obtain the spread of a wide range of solutions,is a multi-objective optimization problem solving and effective method.

关 键 词：多目标优化 背包问题 蚁群算法 多目标蚁群算法 

分 类 号：TP393[自动化与计算机技术—计算机应用技术]