Authors:
Mohammad R. Raeesi N.
and
Ziad Kobti
Affiliation:
University of Windsor, Canada
Keyword(s):
Cultural Algorithm, Multi-Population, Heterogeneous Sub-population, Dynamic Decomposition, Large Scale Global Optimization.
Related
Ontology
Subjects/Areas/Topics:
Artificial Intelligence
;
Co-Evolution and Collective Behavior
;
Computational Intelligence
;
Concurrent Co-Operation
;
Evolutionary Computing
;
Society and Cultural Aspects of Evolution
;
Soft Computing
;
Swarm/Collective Intelligence
Abstract:
Dynamic Heterogeneous Multi-Population Cultural Algorithm (D-HMP-CA) is a novel algorithm to solve global optimization problems. It incorporates a number of local Cultural Algorithms (CAs) and a shared belief space. D-HMP-CA benefits from its dynamic decomposition techniques including the bottom-up and top-down strategies. These techniques divide the problem dimensions into a number of groups which will be assigned to different local CAs. The goal of this article is to evaluate the algorithm scalability. In order to do so, D-HMP-CA is applied on a benchmark of large scale global optimization problems. The results show that the top-down strategy outperforms the bottom-up technique by offering better solutions, while within lower size optimization problems the bottom-up approach presents a better performance. Generally, this evaluation reveals that D-HMP-CA is an efficient method for high dimensional optimization problems due to its computational complexity for both CPU time and memory
usage. Furthermore, it is an effective method such that it offers competitive solutions compared to the state-of-the-art methods.
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