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Chong HY, Yap HJ, Tan SC, Yap KS, Wong SY. Advances of metaheuristic algorithms in training neural networks for industrial applications. Soft comput 2021. [DOI: 10.1007/s00500-021-05886-z] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
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Lu G, Chang W. Comparative analysis of evolutionary algorithms for multiple criteria decision making with interval-valued belief distributions. INTELLIGENT DECISION TECHNOLOGIES 2020. [DOI: 10.3233/idt-190125] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
Abstract
In multiple criteria decision making (MCDM) with interval-valued belief distributions (IVBDs), individual IVBDs on multiple criteria are combined explicitly or implicitly to generate the expected utilities of alternatives, which can be used to make decisions with the aid of decision rules. To analyze an MCDM problem with a large number of criteria and grades used to profile IVBDs, effective algorithms are required to find the solutions to the optimization models within a large feasible region. An important issue is to identify an algorithm suitable for finding accurate solutions within a limited or acceptable time. To address this issue, four representative evolutionary algorithms, including genetic algorithm, differential evolution algorithm, particle swarm optimization algorithm, and gravitational search algorithm, are selected to combine individual IVBDs of alternatives and generate the minimum and maximum expected utilities of alternatives. By performing experiments with different numbers of criteria and grades, a comparative analysis of the four algorithms is provided with the aid of two indicators: accuracy and efficiency. Experimental results indicate that particle swarm optimization algorithm is the best among the four algorithms for combining individual IVBDs and generating the minimum and maximum expected utilities of alternatives.
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Affiliation(s)
- Guangyan Lu
- Department of Operation Management, CRRC Qishuyan Institute Co., Ltd, Changzhou, Jiangsu 213011, China
| | - Wenjun Chang
- School of Management, Hefei University of Technology, Hefei, Anhui 230009, China
- Key Laboratory of Process Optimization and Intelligent Decision-Making, Ministry of Education, Hefei, Anhui 230009, China
- Ministry of Education Engineering Research Center for Intelligent Decision-Making and Information System Technologies, Hefei, Anhui 230009, China
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Eraslan E, Güneşli İ, Khatib W. The evaluation of appropriate office layout design with MCDM techniques. SN APPLIED SCIENCES 2020. [DOI: 10.1007/s42452-020-2181-x] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022] Open
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Fusions and preference relations based on probabilistic interval-valued hesitant fuzzy information in group decision making. Soft comput 2018. [DOI: 10.1007/s00500-018-3465-6] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/28/2022]
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