Authors: Mohamed A. Meselhi ; Ruhul A. Sarker ; Daryl L. Essam and Saber M. Elsayed

Affiliation: School of Engineering and Information Technology, University of New South Wales at Canberra, Canberra, 2600 and Australia

ISBN: 978-989-758-327-8

Keyword(s): Evolutionary Algorithms, Cooperative Coevolution, Problem Decomposition, Large Scale Global Optimization.

Related Ontology Subjects/Areas/Topics: Artificial Intelligence ; Co-Evolution and Collective Behavior ; Computational Intelligence ; Evolutionary Computing ; Soft Computing

Abstract: The curse of dimensionality is considered a main impediment in improving the optimization of large scale problems. An intuitive method to enhance the scalability of evolutionary algorithms is cooperative co-evolution. This method can be used to solve high dimensionality problems through a divide-and-conquer strategy. Nevertheless, its performance deteriorates if there is any interaction between subproblems. Thus, a method that tries to group interdependent variables in the same group is demanded. In addition, the computational cost of current decomposition methods is relatively expensive. In this paper, we propose an enhanced differential grouping (EDG) method, that can efficiently uncover separable and nonseparable variables in the first stage. Then, nonseparable variables are furthermore examined to detect their direct and indirect interdependencies, and all interdependent variables are grouped in the same subproblem. The efficiency of the EDG method was evaluated using large scale global optimization benchmark functions with up to 1000 variables. The numerical experimental results indicate that the EDG method efficiently decomposes benchmark functions with fewer fitness evaluations, in comparison with state-of-the-art methods. Moreover, EDG was integrated with cooperative co-evolution, which shows the efficiency of this method over other decomposition methods. (More)

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Paper citation in several formats:
Meselhi, M.; Sarker, R.; Essam, D. and Elsayed, S. (2018). Enhanced Differential Grouping for Large Scale Optimization.In Proceedings of the 10th International Joint Conference on Computational Intelligence - Volume 1: IJCCI, ISBN 978-989-758-327-8, pages 217-224. DOI: 10.5220/0006938902170224

author={Mohamed A. Meselhi. and Ruhul A. Sarker. and Daryl L. Essam. and Saber M. Elsayed.},
title={Enhanced Differential Grouping for Large Scale Optimization},
booktitle={Proceedings of the 10th International Joint Conference on Computational Intelligence - Volume 1: IJCCI,},


JO - Proceedings of the 10th International Joint Conference on Computational Intelligence - Volume 1: IJCCI,
TI - Enhanced Differential Grouping for Large Scale Optimization
SN - 978-989-758-327-8
AU - Meselhi, M.
AU - Sarker, R.
AU - Essam, D.
AU - Elsayed, S.
PY - 2018
SP - 217
EP - 224
DO - 10.5220/0006938902170224

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