Mathematical Derives of Evolutionary Algorithms for Multiple Criteria Decision Making

Tim Chen, Hendri Daleanu, Chi-Huey Wong* and J.C.-Y. Chen
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Tim Chen: School of Industrial Engineering, West Lafayette, Purdue University, USA School of Computer Science and Informatics, Faculty of Technology, De Montfort University, LE1 9BH Leicester, UK
Hendri Daleanu: Faculty of Engineering, King Abdulaziz University, Jeddah 21589, Saudi Arabia Institute of Space Sciences, Magurele¡VBucharest, Romania
Chi-Huey Wong*: Institute of Biochemistry and Molecular Biology, National Yang-Ming University, Taipei 112, Taiwan Genomics Research Center, Academia Sinica, 128 Academia Road, Section 2, Nankang, Taipei 115, Taiwan
J.C.-Y. Chen: NAAM Research Group, King Abdulaziz University, Jeddah 21589, Saudi Arabia St Petersburg Univ, Dept Math & Mech, Univ Skii 28, St Petersburg 198504, Russia

Sumerianz Journal of Medical and Healthcare, 2019, vol. 2, issue 1, 5-11

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. Optimization models are usually constructed to implement such combination. To analyze a 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. We anticipate experimental results will indicate that particle swarm optimization algorithm is the best one to combine individual IVBDs and generate the minimum and maximum expected utilities of alternatives among the four algorithms.

Keywords: Multiple criteria decision making; Interval-valued belief distribution; Evolutionary algorithm; Accuracy; Efficiency (search for similar items in EconPapers)
Date: 2019
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