A Novel Artificial Bee Colony Algorithm for Numerical Function Optimization

Artificial Bee Colony(ABC) algorithm is a biological-inspired optimization algorithm, which has been shown to be compared with some conventional biological-inspired algorithms, such as Genetic Algorithm(GA), Particle Swarm Optimization(PSO) and Differential Evolution(DE). However, there exists problems such as premature convergence and trapping in local optimal. Inspired by DE, we propose an improved ABC algorithm called pbest-guided ABC (PABC) algorithm by incorporating the information of local best (pbest) solution into the solution search equation to improve the exploitation in the onlookers stage. Moreover, in each iteration, we modify the frequency of perturbation. Finally, we use a more robust calculation to determine and compare the quality of alternative solutions. The experimental results show that PABC algorithm can improve the performance of ABC algorithm.