Comparison of Cuckoo Search, Tabu Search and TS-Simplex algorithms for unconstrained global optimization
Résumé
Metaheuristics Algorithms are widely recognized as one of the most practical approaches for
Global Optimization Problems. This paper presents a comparison between two metaheuristics to
optimize a set of eight standard benchmark functions. Among the most representative single
solution metaheuristics, we selected Tabu Search Algorithm (TSA), to compare with a novel
population-based metaheuristic: Cuckoo Search Algorithm (CSA). Empirical results reveal that
the problem solving success of the TSA was better than the CSA. However, the run-time
complexity for acquiring global minimizer by the Cuckoo Search was generally smaller than the
Tabu Search. Besides, the hybrid TSA-Simplex Algorithm gave superior results in term of
efficiency and run-time complexity compared to CSA or TSA tested alone.