|International Computer Science and Engineering Society (ICSES)|
International Transactions on Evolutionary and Metaheuristic Algorithms
Vol. 3, No. 1, Dec. 2017
Cuckoo Optimization Algorithm (COA) and Its Applications | Special Issue Proposal
Hamid Reza Boveiri a,, Tribeni Prasad Banerjee b
Highlights and Novelties
Most of the combinatorial optimization problems in Engineering and Industry are of such problems cannot be solved using heuristics or exact methods in an efficient way. Meanwhile, another interesting paradigm inspired by biology and artificial life was introduced named Metaheuristics with significant power and potential to cope with such difficult problems. Particle Swarm Optimization (PSO), Ant colony Optimization (ACO), Artificial Bee Colony (ABC), Shuffled Frog Leaping (SFL), Biogeography-Based Optimization (BBO), Teaching-Learning-Based Optimization (TLBO) are considered as some of these metaheuristics. Cuckoo Optimization Algorithm (COA) is also a newly proposed swarm-intelligence-based metaheuristic algorithm first introduced by Rajabioun in 2011, inspired from the exotic lifestyle of a bird family called cuckoo. Indeed, specific egg-laying and breeding characteristics of cuckoos called parasite-brooding is the basis of constituting this novel optimization algorithm. To the best of our knowledge, this metaheuristic algorithm has not been attended and studied a lot since its first introduction in 2011, and a few applications of it on well-known industrial and engineering problems have been reported in the literature so far; hence, we believe that this Special Issue will be going to be an outstanding Resource and Reference on this algorithm and its application.
© Copyright was transferred to International Computer Science and Engineering Society (ICSES) by all the Authors.
Cite this manuscript as
Hamid Reza Boveiri, Tribeni Prasad Banerjee, "Cuckoo Optimization Algorithm (COA) and Its Applications," International Transactions on Evolutionary and Metaheuristic Algorithms (ITEMA), vol. 3, no. 1, pp. 1-3, Dec. 2017.
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