Nashwa Nageh Ahmed

Work place: Suez Canal University, Dept. of Mathematics, Faculty of Science, Ismailia, 41552, Egypt



Research Interests: Bioinformatics, Computer systems and computational processes, Combinatorial Optimization


Nashwa Nageh Ahmed Received the B.Sc. of Science in mathematics from the Suez Canal University in 2009. Currently, she is a demonstrator at the Faculty of Science, Suez Canal University, Ismailia, Egypt. Nashwa research has been focused on meta-heuristics and their applications, global optimization and bioinformatics.

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

By Ahmed Fouad Ali Nashwa Nageh Ahmed

DOI:, 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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