An Improved Artificial Bee Colony Algorithm with Multiple Search Strategy

Abstract

Artificial Bee Colony (ABC) has been applied to solve constrained optimization problems such as green wireless communications, path planning and so on. To solve the problem that ABC algorithm is easy to fall into local optimum, this paper proposes an Improved Artificial Bee Colony (IABC) algorithm with multiple search strategy. An opposition-based learning technique is integrated in initialization phase. Then, in order to speed up convergence rate, each employed bee searches for neighbor with adding global information. Furthermore, multiple search strategy is used to balance the exploitation and exploration during the onlooker bee phase. Inspired by Modification Rate (MR), the solution generation method of new bees whose trail have exceed limit is modified to increase disturbance in scout bee phase. Six benchmark functions are used to test the efficiency and stability of the algorithm, and the simulation results show IABC algorithm performs better than ABC algorithm in high dimensional space.