计算机工程与应用2009，Vol.45Issue(32)：31-34,4.DOI:10.3778/j.issn.1002-8331.2009.32.010
基于模拟退火和文化粒子群的优化算法
Optimization algorithm based on simulated annealing and cultural-based particle swarm
摘要
Abstract
A new hybrid optimization algorithm is presented,which is based on the combination of the simulated annealing and cultural-based particle swarm optimization.To overcome the shortcoming of cultural-based particle swarm optimization that it is easy to trap into local minimum,the simulated annealing algorithm is embedded in the cultural algorithm framework as an evolving course from the knowledge space,which respectively has its own population to evolve independently and parallel.The mechanism improves the population diversity.Finally by comparing the result of the example,it can be found that this proposed algorithm illustrates its higher computational accuracy,convergence rate.关键词
双演化/模拟退火算法/文化算法/混合算法/测试函数Key words
dual evolution/ simulating annealed algorithm /cultural algorithm/ hybrid algorithm/test functions分类
计算机与自动化引用本文复制引用
刘凌子,周永权..基于模拟退火和文化粒子群的优化算法[J].计算机工程与应用,2009,45(32):31-34,4.基金项目
国家自然科学基金(the National Natural Science Foundation of China under Grant No.60461001) （the National Natural Science Foundation of China under Grant No.60461001）
广西自然科学基金(the Natural Science Foundation of Guangxi of China under Grant No.0832084.No.0991086) （the Natural Science Foundation of Guangxi of China under Grant No.0832084.No.0991086）
国家民族事务委员会基金(the State Ethnic Affairs Commission science Foundation under Grant No.08GX01). （the State Ethnic Affairs Commission science Foundation under Grant No.08GX01）
