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Parameter optimization of Cuckoo Search Algorithm for Multi Dimensional Function Optimization Problem
| Communications on Applied Electronics |
| Foundation of Computer Science (FCS), NY, USA |
| Volume 7 - Number 18 |
| Year of Publication: 2018 |
| Authors: Mohammad Shafiul Alam, Samiha Sara Prima, Sanonda Datta Gupta, Jannatun Razia |
10.5120/cae2018652776
|
Mohammad Shafiul Alam, Samiha Sara Prima, Sanonda Datta Gupta, Jannatun Razia . Parameter optimization of Cuckoo Search Algorithm for Multi Dimensional Function Optimization Problem. Communications on Applied Electronics. 7, 18 ( Jul 2018), 6-10. DOI=10.5120/cae2018652776
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.
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