An Interactive Framework to Compare Multi-criteria Optimization Algorithms: Preliminary Results on NSGA-II and MOPSO


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Output type: Journal article

UM6P affiliated Publication?: No

Author list: David F. Dorado-Sevilla, Diego H. Peluffo-Ordóñez, Leandro L. Lorente-Leyva, Erick P. Herrera-Granda, Israel D. Herrera-Granda

Publication year: 2021

Journal: Lecture Notes in Electrical Engineering (1876-1100)

ISSN: 1876-1100

URL: https://link.springer.com/chapter/10.1007/978-***********-4_5

Languages: (EN-US)


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Abstract

A problem of multi-criteria optimization, according to its approach, can mean either minimizing or maximizing a group of at least two objective functions to find the best possible set of solutions. There are several methods of multi-criteria optimization, in which the resulting solutions’ quality varies depending on the method used and the complexity of the posed problem. A bibliographical review allowed us to notice that the methods derived from the evolutionary computation deliver good results and are commonly used in research works. Although comparative studies among these optimization methods have been found, the conclusions that these offer to the reader do not allow us to define a general rule that determines when one method is better than another. Therefore, the choice of a well-adapted optimization method can be a difficult task for non-experts in the field. To implement a graphical interface that allows non-expert users in multi-objective optimization is proposed to interact and compare the performance of the NSGA-II and MOPSO algorithms. It is chosen qualitatively from a group of five preselected algorithms as members of evolutionary algorithms and swarm intelligence. Therefore, a comparison methodology has been proposed to allow the user for analyzing the graphical and numerical results, which will observe the behavior of algorithms and determine the best suited one according to their needs.


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Last updated on 2021-23-07 at 23:16