A hybrid algorithm based on particle swarm and chemical reaction optimization

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• 作者：Tien Trong Nguyen ; ZhiYong Li ; ShiWen Zhang ; Tung Khac Truong
• 关键词：Chemical reaction optimization ; Particle swarm optimization
• 刊名：Expert Systems with Applications
• 出版年：April, 2014
• 年：2014
• 卷：41
• 期：5
• 页码：2134-2143
• 全文大小：1023 K

文摘

In this paper, a hybrid method for optimization is proposed, which combines the two local search operators in chemical reaction optimization with global search ability of for global optimum. This hybrid technique incorporates concepts from chemical reaction optimization and particle swarm optimization, it creates new molecules (particles) either operations as found in chemical reaction optimization or mechanisms of particle swarm optimization. Moreover, some technical bound constraint handling has combined when the particle update in particle swarm optimization. The effects of model parameters like InterRate, 纬, Inertia weight and others parameters on performance are investigated in this paper. The experimental results tested on a set of twenty-three benchmark functions show that a hybrid algorithm based on particle swarm and chemical reaction optimization can outperform chemical reaction optimization algorithm in most of the experiments. Experimental results also indicate average improvement and deviate over chemical reaction optimization in the most of experiments.