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Optimization algorithm based on kinetic-molecular theory 被引量:2

Optimization algorithm based on kinetic-molecular theory
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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. 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.
出处 《Journal of Central South University》 SCIE EI CAS 2013年第12期3504-3512,共9页 中南大学学报(英文版)
基金 Project(61174140)supported by the National Natural Science Foundation of China Project(13JJA002)supported by Hunan Provincial Natural Science Foundation,China Project(20110161110035)supported by the Doctoral Fund of Ministry of Education of China
关键词 optimization algorithm heuristic search algorithm kinetic-molecular theory DIVERSITY CONVERGENCE 优化算法 分子理论 动能 种群多样性 分子吸引力 全局搜索能力 物理原理 局部极值
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