种群动力学优化算法  

Population Dynamics-based Optimization

作　　者：黄光球[1] 李涛[1] 陆秋琴[1] 

机构地区：[1]西安建筑科技大学管理学院,西安710055

出　　处：《计算机科学》2013年第11期280-286,共7页Computer Science

基　　金：陕西省科学技术研究发展计划项目(2013K11-17);陕西省教育厅科技计划项目(12JK0789);陕西重点学科建设专项资金资助项目(E08001);陕西房地产技术经济及管理研究(E08005)资助

摘　　要：为了快速求解大规模复杂优化问题,基于种群动力学理论构造出了可全局收敛的种群动力学优化算法。在该算法中,每个种群对应着优化问题的一个试探解,种群的一个特征对应于试探解的一个变量;采用正交拉丁方原理构造出了种群初始值确定方法,以达到对搜索空间的均衡分散性和整齐可比性覆盖;将任意两种群间的竞争、互利、捕食-被食、融合、突变和选择等行为用于构造种群的进化策略,以使种群的适应度指数要么保持原状不变,要么向好的方向转移,从而确保整个算法的全局收敛性;在种群演变过程中,种群从一种状态转移到另一种状态,实现了种群对优化问题全局最优解的搜索。应用可归约随机矩阵的稳定性条件证明了本算法具有全局收敛性。测试结果表明本算法是高效的。To solve large-scale optimization problems （OP）, a population dynamics-based optimization algorithm with global convergence was constructed based on the population dynamics theory. In the algorithm, each population is just an alternative solution of OP, and a feature attribute of a population corresponds to a variable of an altemative solution. The principle of orthogonal Latin squares is used to produce initial values of populations so as to cover search space with balance dispersion and neat comparability. The competition, mutual-benefit, predator-prey, mergence, mutation and selection behavior between any two populations are used to construct evolution policies of populations so as to ensure population suitability index（PSI）of each population to keep either to stay unchanged or to transfer toward better states, therefore the global convergence is ensured. During evolution process of populations, each population ~ s transferring from one state to another realizes the search for the global optimum solution. The stability condition of a reducible sto- chastic matrix was applied to prove the global convergence of the algorithm. The case study shows that the algorithm is efficient.

关 键 词：进化计算 函数优化 生物地理学优化算法 种群动力学 

分 类 号：TP18[自动化与计算机技术—控制理论与控制工程]