Optimization algorithm based on kinetic-molecular theory

被引:0
|
作者
范朝冬 [1 ]
欧阳红林 [1 ]
张英杰 [2 ]
艾朝阳 [3 ]
机构
[1] College of Electrical and Information Engineering, Hunan University
[2] College of Information Science and Engineering, Hunan University
[3] Institute of Cognitive Science, Hunan University
基金
国家教育部博士点专项基金资助; 中国国家自然科学基金;
关键词
optimization algorithm; heuristic search algorithm; kinetic-molecular theory; diversity; convergence;
D O I
暂无
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
Traditionally, the optimization algorithm based on physics principles has some shortcomings such as low population diversity and susceptibility to local extrema. A new optimization algorithm based on kinetic-molecular theory(KMTOA) is proposed. In the KMTOA three operators are designed: attraction, repulsion and wave. The attraction operator simulates the molecular attraction, with the molecules moving towards the optimal ones, which makes possible the optimization. The repulsion operator simulates the molecular repulsion, with the molecules diverging from the optimal ones. The wave operator simulates the thermal molecules moving irregularly, which enlarges the searching spaces and increases the population diversity and global searching ability. Experimental results indicate that KMTOA prevails over other algorithms in the robustness, solution quality, population diversity and convergence speed.
引用
收藏
页码:3504 / 3512
页数:9
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