基于引力搜索和分布估计的混合离散优化算法  

Hybrid discrete optimization algorithm based on gravity search and estimation of distribution

作　　者：蒋悦[1] 沈冬梅[2] 赵彦[1] 高尚策[2] 

机构地区：[1]江苏信息职业技术学院物联网工程系,江苏无锡214153 [2]东华大学信息科学与技术学院,上海201620

出　　处：《计算机应用》2014年第7期2074-2079,共6页journal of Computer Applications

基　　金：国家自然科学基金资助项目(61203325);上海市晨光计划项目(12CG35);教育部博士点新教师基金资助项目(20120075120004)

摘　　要：针对传统离散引力搜索算法(GSA)容易陷入局部最小解的问题,提出了一种引力搜索和分布估计的混合离散算法GSEDA。通过有效地利用个体在引力搜索的历史统计信息,结合分布估计建立的概率分布模型,生成新的具有全局统计意义的优良解,继而更新搜索群体,使算法搜索更加平衡了空间的开发和探索能力,从而使得算法具有更强的跳出局部最优解的能力。仿真实验结果表明提出的新算法比传统算法具有更好的优化性能和鲁棒性。According to the problem of the traditional Gravitational Search Algorithm （GSA） such as falling into the local minimum point easily, a hybrid algorithm based on Estimation of Distribution （ED） and gravitational search （GSEDA） was proposed. By characterizing the distribution of current solutions found by GSA, ED was used to generate promising solutions based on the constructed probability matrix, thus guiding the search to new solution areas. The proposed GSEDA was able to balance the exploration and exploitation of the search, therefore possessing a better local optima jumping capacity. The experimental results based on the traveling salesman problem indicate that GSEDA performs better than traditional algorithms in terms of solution quality and robustness.

关 键 词：引力搜索算法 分布估计 旅行商问题 NP—hard问题 局部最优解 

分 类 号：TP391.4[自动化与计算机技术—计算机应用技术]