A hybrid algorithm based on particle swarm and chemical reaction optimization |
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| Affiliation: | 1. College of Information Science and Engineering, Hunan University, Changsha 410082, China;2. Faculty of Information Technology, Industrial University of Ho Chi Minh City, Ho Chi Minh, Viet Nam;1. Research Institute of Computer Science, Technical University of Loja, San Cayetano alto, Loja, Ecuador;2. Department of Computing, Polytechnic University of Madrid, Boadilla del Monte, Madrid, Spain;1. Sobey School of Business, Saint Mary’s University, Halifax, NS B3H 2W3, Canada;2. Charlton College of Business, University of Massachusetts Dartmouth, Dartmouth, MA 02747, USA;1. Universidade Federal de Ouro Preto, Computing Department, Ouro Preto, MG, Brazil;2. Universidade Federal de Minas Gerais, Computer Science Department, 31.270-010 Belo Horizonte, MG, Brazil;1. CMR Institute of Technology, AECS Layout, Bangalore, Karnataka 560037, India;2. Christ University, Hosur Road, Bangalore, Karnataka 560029, India;1. EECS Department, University of Toledo, MS 308, 2801 W. Bancroft St., Toledo, OH 43606, United States;2. School of Computing Sciences and Informatics, University of Cincinnati, Cincinnati, OH 45221, United States;1. Department of Construction Engineering, National Taiwan University of Science and Technology, 43, Sec. 4, Keelung Rd., Taipei 106, Taiwan |
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| Abstract: | 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, γ, 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. |
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| Keywords: | Chemical reaction optimization Particle swarm optimization |
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