An interactive fuzzy multi-objective optimization method for engineering design |
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| Affiliation: | 1. School of Mechatronics Engineering, University of Electronic Science and Technology of China, Chengdu, Sichuan 610054, China;2. School of Mechanical & Electronical Engineering, Jiangxi University of Science and Technology, Ganzhou, Jiangxi 341000, China;3. Department of Mechanical and Aerospace Engineering, University of Missouri-Rolla, MO 65409, USA;1. Departamento de Estadística e I.O., Universidad de Sevilla, Spain;2. Instituto de Alta Investigación, Universidad de Tarapacá, Casilla 7D, Arica, Chile;1. Department of Applied Mathematics with Oceanology and Computer Programming, Vidyasagar University, Midnapore, West Bengal 721 102, India;2. Department of Applied Science, Haldia Institute of Technology, Haldia, Purba Medinipur 721657, West Bengal, India;1. Department of Mathematics, Adamas Institute of Technology, North 24-Parganas-700126, West Bengal, India;2. Department of Mathematics, Indian Institute of Engineering Science and Technology, Shibpur, Howrah- 711103, West Bengal, India;3. Department of Civil Engineering, Indian Institute of Engineering Science and Technology, Shibpur, Howrah- 711103, West Bengal, India;1. Faculty of Industrial Engineering, Urmia University of Technology, Urmia, Iran;2. Faculty of Engineering, Urmia University, Urmia, Iran;1. Department of Natural Resources and Environment engineering, Faculty of Agricultural Sciences, Payame Noor University, Iran;2. Department of Forestry, Faculty of Natural Resources, University of Guilan, Sowmeh Sara, P.O. Box 1144, Iran |
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| Abstract: | The coupling of performance functions due to common design variables and uncertainties in an engineering design process will result in difficulties in optimization design problems, such as poor collaboration among design objectives and poor resolution of design conflicts. To handle these problems, a fuzzy interactive multi-objective optimization model is developed based on Pareto solutions, where the metric function and some additional constraints are added to ensure the collaboration among design objectives. The trade-off matrix at the Pareto solutions was developed, and the method for selecting weighting coefficients of optimization objectives is also provided. The proposed method can generate a Pareto optimal set with the maximum satisfaction degree and the minimum distance from ideal solution. The favorable optimal solution can be then selected from the Pareto optimal set by analyzing the trade-off matrix and collaborative sensitivity. Two examples are presented to illustrate the proposed method. |
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