Artificial bee colony algorithm inspired by particle swarm optimization and differential evolution

Abstract

Concerning the problem that Artificial Bee Colony(ABC) is good at exploring but lack of exploitation,two new solution search strategies named PSO-DE-PABC and PSO-DE-GABC were proposed based on Particle Swarm Optimization(PSO) and Differential Evolution(DE). PSO-DE-PABC generated new candidate position around the random particle to improve divergence. PSO-DE-GABC generated new candidate position around the global best solution to accelerate the convergence,and differential vectors were also used to increase the divergence. Besides,Dimension Factor(DF) was introduced to control the search rate of the algorithms. A new scout strategy considering current swarm state was used to replace the original random scout strategy to enhance the local search ability. Comparison with basic ABC,GABC(Gbestguided ABC) and ABC / best algorithm was given on 10 groups of standard benchmark function. The results show that PSO-DEGABC and PSO-DE-PABC have better convergence rate and accuracy.