An improved artificial bee colony algorithm for solving constrained optimization problems

The artificial bee colony (ABC) algorithm is a global stochastic optimization algorithm inspired by simulating the foraging behavior of honey bees. It has been successfully applied to solve the constrained optimization problems (COPs) with a constraint handling technique (Deb's rules). However, it may also lead to premature convergence. In order to improve this problem, we propose an improved artificial bee colony (I-ABC) algorithm for COPs. In I-ABC algorithm, we firstly relax the Deb's rules by introducing the approximate feasible solutions to suitably utilize the information of the infeasible solutions with better objective function value and small violation. Next, we construct a selection strategy based on rank selection and design a search mechanism using the information of the best-so-far solution to balance the exploration and the exploitation at different stages. In addition, periodic boundary handling mode is used to repair invalid solutions. To verify the performance of I-ABC algorithm, 24 benchmark problems are employed and two comparison experiments have been carried out. The numerical results show that the proposed I-ABC algorithm has an outstanding performance for the COPs.