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Comparison of Two Random Weight Generators for Multi-Objective Optimization
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Author(s): Victor M. Carrillo (Universidad Autónoma de Ciudad Juárez, Mexico)and German Almanza (Universidad Autónoma de Ciudad Juárez, Mexico)
Copyright: 2016
Pages: 16
Source title:
Handbook of Research on Military, Aeronautical, and Maritime Logistics and Operations
Source Author(s)/Editor(s): Alberto Ochoa-Zezzatti (Juarez City University, Mexico), Jöns Sánchez (Consejo Nacional De Ciencie Y Tecnologia (CONACYT), Mexico), Miguel Gastón Cedillo-Campos (Transportation Systems and Logistics National Laboratory, Mexican Institute of Transportation, Mexico)and Margain de Lourdes (Polytechnic University of Aguascalientes, Mexico)
DOI: 10.4018/978-1-4666-9779-9.ch011
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Abstract
There exist two general approaches to solve multiple objective problems. The first approach belongs to the classical mathematical methods: The weighted sum method, goal programming, or utility functions methods pertain to this approach. The output of mathematical methods is a single optimal solution. In the second approach are the heuristic methods, like the multiple objective evolutionary algorithms that offer the decision maker a set of optimal solutions usually called non- dominated or, Pareto-optimal solutions. This set is usually very large and the decision maker faces the problem of reducing the size of this set to a manageable number of solutions to analyze. In this paper the second approach is used to reduce the Pareto front using two weights generator for the non-numerical ranking preferences method and their performance is compared.
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