A novel artificial bee colony algorithm based on modified search strategy and generalized opposition-based learning

Abstract

Artificial bee colony (ABC) algorithm, which is inspired by the foraging behavior of honey bee swarm, is a biological-inspired optimization algorithm. It shows more effective than genetic algorithm (GA), particle swarm optimization (PSO) and differential evolution (DE). However, ABC algorithm can sometimes be slow to converge, and it is good at exploration but poor at exploitation regarding its solution search equation. To address these concerning issues, we propose a novel search strategy at the employed bees stage by introducing generalized opposition-based learning method as a search mechanism and an improved solution search equation by taking advantages of the local best solution at the onlookers stage. Both operations can balance the exploration and the exploitation for the proposed algorithm. Then, in order to enhance the global convergence, we modify dynamically frequency of perturbation at each iteration. In addition, we use a more robust calculation to determine and compare the quality of alternative solutions. Experiments are conducted on a set of 21 benchmark functions. The experimental results show that the proposed algorithm can outperform ABC-based algorithms and other significant evolutionary optimizers in solving complex numerical optimization problems.