Parallelized Cuckoo Search Algorithm for Unconstrained Optimization

Modifications that introduce parallelization of standard cuckoo search algorithm are proposed in this paper. Basic form of the cuckoo search algorithm has already shown great potential for optimization problems, especially when applied to unconstrained continuous functions. In this paper two aspects of parallelization are proposed. The first one addresses the performance issue, while the second one deals with quality of results. Multicore processors became standard today. When different runs of algorithm execute within different threads, better performance can be reached. Second issue refers to multiple flocks approach that combines search results from two or more flocks. Set of well-known unconstrained continuous benchmark function is used to illustrate testing results of the proposed parallelized cuckoo search algorithm. Key-Words: Cuckoo search, Parallelization, Unconstrained optimization, Swarm intelligence, Metaheuristics