Artificial Bee Colony with Mean Mutation Operator for Better Exploitation

ABC is an optimization technique, used in finding the best solution from all feasible solutions. However, there is still an insufficiency in ABC regarding improvement in exploitation and convergence speed. In order to improve the performance of ABC we used mean mutation operator (MMO), which uses a linear combination of Gaussian and Cauchy distributions. This convoluted distribution produces larger mutations than the Gaussian distribution, and smaller mutations than the Cauchy distribution, which in simpler words justifies/balances exploration and exploitation in ABC. Experiments are conducted on a set of 6 benchmark functions. The results demonstrate good performance of proposed variant in solving numerical optimization problems when compared with three ABC-based algorithms.