An Enhanced Exploitation Artificial Bee Colony Algorithm in Automatic Functional Approximations

摘要

Aiming at the drawback of artificial bee colony algorithm (ABC) with slow convergence speed and weak exploitation capacity, an enhanced exploitation artificial bee colony algorithm is proposed, EeABC for short. Firstly, a generalized opposition-based learning strategy (GOBL) is employed when initial population is produced for obtaining an evenly distributed population. Subsequently, inspired by the differential evolution (DE), two new search equations are proposed, where the one is guided by the best individuals in the next generation to strengthen exploitation and the other is to avoid premature convergence. Meanwhile, the distinction between the employed bee and the onlooker bee is not made, unified as a bee and controlled by the probability P. The performance of proposed approach was examined on 14 benchmark functions, and results are compared with basic ABC and other ABC variants. As documented in the experimental results, the proposed algorithm has good optimization performance and can improve both the accuracy and the convergence speed.