Retracted: Improved artificial bee colony algorithm with opposition‐based learning

Abstract

The artificial bee colony (ABC) algorithm is a biological‐inspired optimisation algorithm proposed by Karaboga. Since its solution search equation is good at exploration but poor at exploitation, the ABC algorithm converges slowly and is easy to fall into local optimum. Inspired by opposition‐based learning (OBL), the authors propose an improved ABC algorithm called opposition‐based learning ABC (OLABC). In OLABC, firstly, the population would be initialised using OBL. Secondly, to ensure the diversity of the population during the iterative process, the solution search equation is employed to bee phase would be improved. Generate the opposite solution when the fitness value of the newly generated solution is smaller than the current solution, and then apply the greedy selection strategy to update the solution. Thirdly, the adaptive weight strategy is used to dynamically adjust the weight, balancing the global exploration and local exploitation capabilities of the algorithm. Experiments on a set of benchmark functions show that OLABC has better convergence speed and optimisation precision than the compared algorithms.

Keywords

• global exploration
• adaptive weight strategy
• iterative process
• greedy selection strategy
• opposition‐based learning ABC
• local exploitation capabilities