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Optimization algorithm based on kinetic-molecular theory
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AI
DOI：10.1007/s11771-013-1875-2.png)
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Abstract
En 中文
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.
Keywords:
optimization algorithm
heuristic search algorithm
kinetic-molecular theory
diversity
convergence
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