Nonlinear continuous multi-response problems: a novel two-phase hybrid genetic based metaheuristic |
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| Authors: | Saeid Fallah-Jamshidi Maghsoud Amiri Neda Karimi |
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| Affiliation: | 1. Department of Industrial and Mechanical Engineering, Qazvin Islamic Azad University, Qazvin, Iran;2. Department of Industrial Management, Management and Accounting Faculty, Allameh Tabatabaei University, Tehran, Iran;1. Department of Chemical Engineering and Materials Science, Amrita University, Coimbatore, India;2. Department of Sciences, Amrita University, Coimbatore, India;3. Department of Chemical Engineering, SSN College of Engineering, Chennai 603 110, India;1. Beijing Key Lab for Source Control Technology of Water Pollution, Beijing Forestry University, Beijing 100083, China;2. School of Environment and Resource, Renmin University of China, Beijing 100872, China;3. ShenZhen Techand Ecology & Environment CO. LTD., Shenzhen 518040, China;1. College of Forestry, Northwest A&F University, 3 Taicheng Road, Yangling 712100, China;2. Beijing Research and Development Center for Grass and Environment, Beijing Academy of Agriculture and Forestry Sciences, Beijing 100097, China |
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| Abstract: | Generally the most real world production systems are tackling several different responses and the problem is optimizing these responses concurrently. This study strives to present a new two-phase hybrid genetic based metaheuristic for optimizing nonlinear continuous multi-response problems. Premature convergence and getting stuck in local optima, which makes the algorithm time consuming, are common problems dealing with genetic algorithms (GAs). So we hybridize GA with a clustering approach and particle swarm optimization algorithm (PSO) to make a balanced relationship between time consuming and premature termination. The proposed algorithm also tries to find Ideal Points (IPs) for response functions. IPs are considered as improvement measures that determine when PSO should start. PSO based local search exploit Pareto archive solutions to enhance performance of the algorithm by expanding the search space. Since there is no standard benchmark in this field, we use two case studies from distinguished paper in multi-response optimization and compare the results with some of the mentioned algorithms in the literature. Results show the outperformance of the proposed algorithm than all of them. |
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