Directed Artificial Bee Colony algorithm with revamped search strategy to solve global numerical optimization problems

Abstract

Artificial Bee Colony algorithm (ABC) is inspired by behavior of food foraging of honeybees to solve the NP-Hard problems using optimization model which is one among the swarm intelligence algorithms. ABC is a widespread optimization algorithm to obtain the best solution from feasible solutions in the search space and strive harder than other existing population-based algorithms. However, in diversification process ABC algorithm shows good performance but lacks in intensification process and slows to convergence towards an optimal solution because of its search equations. In this work, the authors proposed an improvised solution search strategy at employed bee phase and onlooker bee phase by considering the advantages of the local-best, neighbor-best, and iteration-best solutions. Thus, the obtained candidate solutions are closer to the best solution by providing directional information to ABC algorithms. The search radius for new candidate solutions is adjusted in scout bee phase which facilitates to move towards global convergence. Thus, the process of diversification and intensification is balanced in this work. Finally, to assess the performance of the proposed algorithm, 20 numerical benchmarks functions are used. To show the significance of the proposed methodology it has been tested with Combined Heat and Economic Power Dispatch (CHPED) problem. The empirical result exhibits that the proposed algorithm provides higher quality solutions and outperform with original ABC algorithm for solving numerical optimization problems.