An Enhanced Genetic Algorithm using Directional-Based Crossover and normal mutation For Global Optimization Problems

Ahmed M.Abdelkhalek; Ammar  Mohammed; Mahmoud Attia; Niveen Badra

doi:10.19139/soic-2310-5070-1796

Ahmed M.Abdelkhalek Faculty of Engineering - Ain shams University
Ammar Mohammed Department of Computer Science, Faculty of Graduate Studies for Statistical Research, Cairo University
Mahmoud A. Attia Department of Electrical Power and Mechanics, Faculty of Engineering, Ain shams University
Niveen Badra Department of Physics and Engineering Mathematics, Faculty of Engineering, Ain Shams University

DOI: https://doi.org/10.19139/soic-2310-5070-1796

Keywords: Directional-based crossover, Global Optimization, Genetic Algorithm, Normal Mutation

Abstract

Global optimization has been employed in many practical modeling processes. Using gradient methods to solve optimization problems may be computationally inefficient and time-consuming, particularly when convexity or differentiability is not guaranteed. On the other hand, nature-inspired techniques offer an effective gradient-free approach for solving complex, non-convex, or non-differentiable problems. Genetic algorithms are one of the most effective and widely used nature-inspired techniques. However, canonical genetic algorithms do not always guarantee convergence to the optimum point owing to the stochastic nature of the genetic operators, and typically require more work to ensure convergence and increase performance. Improving the genetic operators remains an open issue and usually involves a trade-off between the speed of convergence and searchability. In this study, we propose an enhanced genetic algorithm that relies on directional-based crossover and normal mutation operators to increase the speed of convergence while preserving searchability. The proposed algorithm is evaluated using a set of 40 typical benchmark functions in two dimensions. In addition, to examine its performance at higher dimensions, 16 functions from the test set were tested at 10 and 100 dimensions. The evaluation results of the proposed algorithm are compared to the outcomes of three modern optimization algorithms, namely (Whale optimization algorithm, Teacher-Learner based algorithm, and Covariance matrix adaptation evolution strategy). The results revealed that the proposed algorithm outperformed the conventional algorithms at lower dimensions in all test functions and showed a relatively better performance than the other algorithms at higher dimensions.

Author Biographies

Ammar Mohammed, Department of Computer Science, Faculty of Graduate Studies for Statistical Research, Cairo University

AMMAR MOHAMMED is an associate professorof Computer Science at both Cairo Universityand Misr International University in Egypt. Hereceived his Bachelor’s and Master’s degrees inComputer Science from Cairo University, Egyptand his Ph.D in Computer Science from the Universityof Koblenz-landau, Germany in 2010. Heworked as a researcher and research fellow at theArtificial Intelligence (AI) research group, Universityof Koblenz-landau, Germany. He superviseda group of PhD and Master students and established the Machine/Deeplearning research group in the Department of Computer Science, Facultyof Graduate Studies for Statistical Research, Cairo University. His researchinterests include machine and deep learning techniques, methods, algorithmsand their applications in Several domains.

Mahmoud A. Attia, Department of Electrical Power and Mechanics, Faculty of Engineering, Ain shams University

MAHMOUD A. ATTIA is currently working asan Associate Professor of electrical power engineeringat Ain Shams University. He receivedB.Sc., M.Sc. and Ph.D. in electrical engineeringfrom Ain Shams University (ASU), Egypt in2005, 2010 and 2015 respectively. He has joinedASU teaching stuff since 2007. He authored manyjournal and conference papers. He is a reviewerfor power component and systems journal, AinShams Engineering Journal Elsevier and InternationalTransactions on Electrical Energy Systems. 2009, He was in Technicalcommittee of ASU international conference ASCEE-3. He was a Memberof "Continuous Improvement and Quality Assurance Unit of Faculty ofengineering ASU" till 2017. He is the author of books ’Optimal Allocationof FACTS Devices in Electrical Power Systems: A Genetic Algorithm BasedApproach’, in April 2013 and ‘Enhancing Power System Performance withGrowing Wind Power Penetration: optimal Allocation of FACTS’, in July2015 LAP LAMBERT Academic Publishing. He contributed in book ‘SustainableEnergy Technologies and Systems’, LAP LAMBERT AcademicPublishing, 2019. He is an editorial member of i-manager’s Journal onCircuits and Systems. He is an editor in chief of i-manager’s Journal onInstrumentation and Control Engineering. His research areas include theapplications of artificial intelligence, evolutionary and heuristic optimizationtechniques to power system operation, planning, and control.

Niveen Badra, Department of Physics and Engineering Mathematics, Faculty of Engineering, Ain Shams University

NIVEEN BADRA is Professor of EngineeringMathematics in 2008. Her interests are in Optimizationand Engineering Applications, AppliedStatistics with engineering applications, Numericaltechniques with engineering applications, Trafficvolume forecasting and Operational management.

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