Comparison of Cuckoo Search, Tabu Search and TS-Simplex algorithms for unconstrained global optimization

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.