Dimension-by-dimension enhanced cuckoo search algorithm for global optimization
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Cuckoo search (CS) algorithm is an efficient meta-heuristic algorithm that has been successfully applied in many fields. However, the algorithm uses the whole updating and evaluating strategy on solutions. For solving multi-dimensional optimization problems, solutions with partial dimension evolution may be discarded due to mutual interference among dimensions. Therefore, this strategy may deteriorate the quality solution and convergence rate of algorithm. To overcome this defect and enhance the algorithm performance, a dimension-by-dimension enhanced CS algorithm is proposed. In the global explorative random walk, the improved algorithm uses the dimension-by-dimension updating and evaluating strategy on solutions. This strategy combines the updated values of each dimension with the values of other dimensions into a new solution. In addition, a greedy strategy is adopted to accept new solution and the search center is set as the current optimal solution. The proposed algorithm was tested on fourteen well-known benchmark functions. The numerical results show that the improved algorithm can effectively enhance the quality solution and convergence rate for the global optimization problems.
KeywordsCuckoo search (CS) Dimension-by-dimension enhanced Meta-heuristic Lévy flights
This work is supported by the National Natural Science Foundation of China (No. 51705531) and the Jiangsu Province Science Foundation for Youths (No. BK20150724).
Compliance with ethical standards
Conflict of interest
Liang Chen declares that he has no conflict of interest. Houqing Lu declares that he has no conflict of interest. Hongwei Li declares that he has no conflict of interest. Guojun wang declares that he has no conflict of interest.
This article does not contain any studies with human participants or animals performed by any of the authors.
Informed consent was obtained from all individual participants included in the study.
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