Hybridisation of classical unidimensional search with ABC to improve exploitation capability
Pranav Dass, Shimpi Singh Jadon, Harish Kumar Sharma, Jagdish Chand Bansal, Kendall E. Nygard · International Journal of Artificial Intelligence and Soft Computing · 2015
Artificial bee colony (ABC) optimisation algorithm is relatively a recent, fast and easy to implement population-based meta heuristic for optimisation. ABC has been proved a competitive algorithm with some popular swarm intelligence-based algorithms such as particle swarm optimisation, firefly algorithm and ant colony optimisation. However, it is observed that ABC algorithm is better at exploration but poor at exploitation. Due to large step size, the solution search equation of ABC has enough chance to skip the optimum. In order to balance this, ABC is hybridised with a local search called as classical unidimensional search (CUS). The proposed algorithm is named as hybridised ABC (HABC). In HABC, best solution of each iteration is further exploited in both its positive and negative direction in a predefined range which enhances the exploitation in ABC. The experiments are carried out on 15 test problems of different complexities and dimensions in order to prove the efficiency of proposed algorithm and compared with ABC. The results shows that hybridisation of CUS with ABC improves the performance of ABC.