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Assessing the Performance of Electimize in Solving NP-Complete Optimization Problems  

Mohamed Abdel-Raheem1, Ahmed Khalafallah2

1Assistant Professor, Dept. of Engineering Technology

Missouri Western State University

Saint Joseph, MO, USA



2Assistant Professor; Dept. of Archit. & Manuf. Sciences

Western Kentucky University

 Bowling Green, KY, USA


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.
Keywords : optimization; Electimize; evolutionary algorithms; ; NP-complete; traveling salesman problem

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