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Assessing the Performance of Electimize in Solving NP-Complete Optimization Problems
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Mohamed Abdel-Raheem1, Ahmed Khalafallah2
1Assistant Professor, Dept. of Engineering Technology
Missouri Western State University
Saint Joseph, MO, USA
e-mail: maraheem@knights.ucf.edu
2Assistant Professor; Dept. of Archit. & Manuf. Sciences
Western Kentucky University
Bowling Green, KY, USA
e-mail: ahmed.khalafallah@wku.edu
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Abstract
.Electimize is a new evolutionary algorithm (EA) algorithm that was introduced to overcome some limitations of existing evolutionary algorithms. Electimize simulates the phenomenon of the electrical current conductivity through the representation of solution strings as wires in closed electric circuits. Unlike some EAs, Electimize has the ability to assess the quality of each value in the solution string independently. The assessment of values in potential solution is based on Ohm’s law and Kirchhoff’s rule.
One of the primary objectives of developing Electimize is to devise additional capabilities that would enable the algorithm to solve a wide range of discrete optimization problems. Specifically, this paper aims to: 1) assess the capabilities of the algorithm in solving a challenging class of discrete optimization problems, namely, NP-complete optimization problems, 2) compare the performance of Electimize to other EAs that were used to solve this class of problems. For this purpose, an instant (Bayg29) of the traveling salesman problem (TSP) was selected for the testing, application and comparison purposes.
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Keywords
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optimization; Electimize; evolutionary algorithms; ; NP-complete; traveling salesman problem
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URL: http://dx.doi.org/10.7321/jscse.v3.n3.54
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