Personal Best Cuckoo Search Algorithm for Global Optimization

Abstract

Real-life optimization problems demand robust algorithms that perform efficient search in the environment without trapping in local optimal locations. Such algorithms are equipped with balanced explorative and exploitative capabilities. Cuckoo search (CS) algorithm is also one of such optimization algorithms, which is inspired from nature. Despite effective search strategies using Lévy flights and solution switching approach, CS suffers from lack of population diversity when implemented in hard optimization problems. In this paper, enhanced local and global search strategies have been proposed in CS algorithm. The proposed variant employs personal best information in solution generation process, hence called Personal Best Cuckoo Search (pBestCS). Moreover, instead of constant value for switching parameter, pBestCS dynamically updates switching parameter as the iterations proceed. The prior approach enhances local search ability, whereas the later modification enforces effective global search ability in the algorithm. The experimental results on both unimodal and multimodal test functions with different dimensionalities validated the efficiency of the proposed modification. Based on comprehensive statistical analysis and comparisons, pBestCS outperformed the standard CS algorithm, as well as, other popular swarm-based metaheuristic algorithms particle swarm optimization (PSO) and artificial bee colony (ABC).