Ahmed Fouad Ali

Work place: Suez Canal University, Dept. of Computer Science, Faculty of Computers and Information, Ismailia, 41552, Egypt

E-mail: ahmed_fouad@ci.suez.edu.eg


Research Interests: Computational Learning Theory, Combinatorial Optimization


Ahmed Fouad Ali Received the B.Sc., M.Sc. and Ph.D. degrees in computer science from the Assiut University in 1998, 2006 and 2011, respectively. He was a Postdoctoral Fellow at Thompson Rivers University, Kamloops, BC Canada for one year. In addition, he is an Assistant Professor at the Faculty of Computers and Informatics, Suez Canal University, Ismailia, Egypt.

He served as a member of computer science department Council from 2014-2015. He worked as director of digital library unit at Suez Canal University; he is a member in SRGE (Scientific Research Group in Egypt). He also served as a technical program committee member and reviewer in worldwide conferences. Dr. Ali research has been focused on meta-heuristics and their applications, global optimization, machine learning.

Author Articles
Differential Evolution Algorithm with Space Partitioning for Large-Scale Optimization Problems

By Ahmed Fouad Ali Nashwa Nageh Ahmed

DOI: https://doi.org/10.5815/ijisa.2015.11.07, Pub. Date: 8 Oct. 2015

Differential evolution algorithm (DE) constitutes one of the most applied meta-heuristics algorithm for solving global optimization problems. However, the contributions of applying DE for large-scale global optimization problems are still limited compared with those problems for low and middle dimensions. DE suffers from slow convergence and stagnation, specifically when it applies to solve global optimization problems with high dimensions. In this paper, we propose a new differential evolution algorithm to solve large-scale optimization problems. The proposed algorithm is called differential evolution with space partitioning (DESP). In DESP algorithm, the search variables are divided into small groups of partitions. Each partition contains a certain number of variables and this partition is manipulated as a subspace in the search process. Selecting different subspaces in consequent iterations maintains the search diversity. Moreover, searching a limited number of variables in each partition prevents the DESP algorithm from wandering in the search space especially in large-scale spaces. The proposed algorithm is tested on 15 large- scale benchmark functions and the obtained results are compared against the results of three variants DE algorithms. The results show that the proposed algorithm is a promising algorithm and can obtain the optimal or near optimal solutions in a reasonable time.

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