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Comparative Study of Cuckoo Inspired Metaheuristics Applying to Knapsack Problems

International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Year of Publication: 2016
Amira Gherboudj

Amira Gherboudj. Comparative Study of Cuckoo Inspired Metaheuristics Applying to Knapsack Problems. International Journal of Computer Applications 155(12):25-31, December 2016. BibTeX

	author = {Amira Gherboudj},
	title = {Comparative Study of Cuckoo Inspired Metaheuristics Applying to Knapsack Problems},
	journal = {International Journal of Computer Applications},
	issue_date = {December 2016},
	volume = {155},
	number = {12},
	month = {Dec},
	year = {2016},
	issn = {0975-8887},
	pages = {25-31},
	numpages = {7},
	url = {},
	doi = {10.5120/ijca2016912508},
	publisher = {Foundation of Computer Science (FCS), NY, USA},
	address = {New York, USA}


Cuckoo Optimization Algorithm (COA) and Cuckoo Search Algorithm (CS) are two population-based metaheuristics. They are based on the cuckoo’s behavior in their lifestyle and their characteristics in egg laying and breeding. Both algorithms are proposed for continuous optimization problems. In this paper, we propose a comparative study of COA and CS. For this we have proposed a binary version of COA (called BCOA) algorithm using the Sigmoid function like we have do in a later work, in which we have proposed a binary version of CS algorithm that we have called BCS. In aim to compare the efficiency of the too algorithms, we have used the proposed BCOA to resolve knapsack problem (KP) and Multidimensional knapsack problem (MKP) problems and we have compared the obtained results with those obtained by BCS.


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Combinatorial optimization, Cuckoo Optimization Algorithm, Cuckoo Search, Binary Cuckoo Optimization Algorithm, Binary Cuckoo Search, knapsack problem, Multidimensional knapsack problem.