An opposition-based hybrid Artificial Bee Colony with differential evolution

This paper presents an opposition-based hybrid Artificial Bee Colony (ABC) with Differential Evolution (DE) algorithm for solving continuous problems. The proposed algorithm, called OABCDE, employs an efficient mutation operation of DE and a crossover-like mechanism to enhance the convergence of ABC without adding parameters. The opposition-based learning routine is periodically executed to prevent being trapped in local optima. The numerical experimentation uses 16 widely-accepted nonlinear benchmark functions of different characteristics and tests at 30, 60 and 100 dimensions. The results demonstrate that OABCDE achieves a superior performance compared to the advance qABC [9] (a recent hybrid ABC and DE) and Opposition-based DE [15].