An Adaptive Scaling Cuckoo Search Algorithm

The cuckoo search algorithm (CSA) is a natureinspired metaheuristic algorithm based on the parasitic behaviour of the cuckoo species, and it has received much attention for solving optimization problems. Nevertheless, the CSA still has some limitations when applied to optimization problems, such as easily falling into local optima and converging slowly. In this paper, we propose a modified local search method with an adaptive boundary scaling mechanism using a chaotic map, which calculates the search radius depending on the position of the current population. Thus, the search ability of the traditional CSA is improved for optimization purposes. To further verify the stability and effectiveness of the proposed adaptive boundary scaling algorithm (ACSA), we compare it with the traditional CSA and nonmodified chaos-embedded CSA (CCSA) on 25 test benchmark functions. The experimental and statistical test results show that the ACSA outperforms its competitors, which means that the proposed algorithm can perform better than these other approaches in terms of their search capabilities for optimization problems.