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Al-khafaji Amen
University of Technology
Computer Science & IT Engineering

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Multi-Operator Genetic Algorithm for Dynamic Optimization Problems Al-khafaji Amen
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 6, No 3: September 2017
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (399.186 KB) | DOI: 10.11591/ijai.v6.i3.pp139-142

Abstract

Maintaining population diversity is the most notable challenge in solving dynamic optimization problems (DOPs). Therefore, the objective of an efficient dynamic optimization algorithm is to track the optimum in these uncertain environments, and to locate the best solution. In this work, we propose a framework that is based on multi operators embedded in genetic algorithms (GA) and these operators are heuristic and arithmetic crossovers operators. The rationale behind this is to address the convergence problem and to maintain the diversity. The performance of the proposed framework is tested on the well-known dynamic optimization functions i.e., OneMax, Plateau, Royal Road and Deceptive. Empirical results show the superiority of the proposed algorithm when compared to state-of-the-art algorithms from the literature.
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