Abstract

In this paper, the algorithmic concepts of the CucKoo-search (CK), Particle Swarm Optimization (PSO), Differential Evolution (DE), and Artificial Bee Colony (ABC), algorithms have been analyzed. The mathematical optimization problem solving successes of the mentioned algorithms have also been compared statistically by testing more than 20 different benchmark functions. Pragmatic results reveal that the problem solving success of the CK algorithm is very close to the DE algorithm. The run-time complexity and the required function-evaluation number for acquiring global minimiser by the DE algorithm are normally smaller than the relationship algorithms. The performances of the CK and PSO algorithms are statistically closer to the performance of the DE algorithm than the ABC algorithm. The CK and DE algorithms deliver more robust and précis results than the PSO and ABC algorithms.

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