Shehab, Mohammad and Khader, Ahamad Tajudin and Laouchedi, Makhlouf (2018) A Hybrid Method Based on Cuckoo Search Algorithm for Global Optimization Problems. Journal of Information and Communication Technology, 17 (3). pp. 469-491. ISSN 2180-3862

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Abstract

Cuckoo search algorithm is considered one of the promising metaheuristic algorithms applied to solve numerous problems in different fields. However, it undergoes the premature convergence problem for high dimensional problems because the algorithm converges rapidly. Therefore, we proposed a robust approach to solve this issue by hybridizing optimization algorithm, which is a combination of Cuckoo search algorithm and Hill climbing called CSAHC discovers many local optimum traps by using local and global searches, although the local search method is trapped at the local minimum point. In other words, CSAHC has the ability to balance between the global exploration of the CSA and the deep exploitation of the HC method. The validation of the performance is determined by applying 13 benchmarks. The results of experimental simulations prove the improvement in the efficiency and the effect of the cooperation strategy and the promising of CSAHC.

Item Type:	Article
Uncontrolled Keywords:	Cuckoo search algorithm, Hill climbing, optimization problems, slow convergence, exploration and exploitation
Subjects:	Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Divisions:	School of Computing
Depositing User:	Mrs Nurin Jazlina Hamid
Date Deposited:	09 Feb 2023 02:35
Last Modified:	09 Feb 2023 02:35
URI:	https://repo.uum.edu.my/id/eprint/29162

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