Parameter optimization of Cuckoo Search Algorithm for Multi Dimensional Function Optimization Problem

Abstract

The Cuckoo Search Algorithm is a recently developed nature inspired meta heuristic algorithm, which is established on the breeding behavior of Cuckoo species. Cuckoo search can be applied on a large variety of optimization problems. The main advantage of this search algorithm is its simplicity and better performance than many other agent or population based meta heuristic algorithms. The algorithm uses only one controlling parameter p, which makes it easier to implement and control. This parameter p, combined with the random walk mutations implemented by Lévy Flights, can control the performance and degree of exploration and exploitation of the algorithm. In this paper we have conducted a few experiments on Cuckoo Search algorithm with Lévy flights to discover the necessary conditions needed for the better performance of the algorithm. For this purpose we have taken different values of the controlling parameter p and observed the performance of the algorithm on benchmark problems, as well as its exploration and exploitation characteristics over different groups of benchmark functions.

Analysis of Impact Behaviour of TIG Weldment by Using Hybrid RSM and CSO

“…The main advantage of this search algorithm is its simplicity, rapid convergence and efficiency in solving highly non-linear optimization problems with real-world engineering applications [24,25] and better performance than many other agent or population based meta heuristic algorithms like, genetic algorithm, particle swarm optimization etc. [26].…”