Chaotic Artificial Bee Colony algorithm based on memory for solving dynamic optimization problems

Artificial Bee Colony Algorithm(ABC) is one of the swarm intelligence optimization algorithms that is extensively used for the goals and applications static. Many practical, real-world applications, nevertheless, are dynamic. Thus we need to get used optimization algorithms that could be solved problems in dynamic environments as well. Dynamic optimization problems where change(s) may occur through the time. In this paper we proposed one approach based on chaotic ABC combined with explicit memory method, for solving dynamic optimization problems. In this proposed algorithm, we used the explicit memory for store the aging best solution for the maintaining diversity in the population. Use the aging best solution and diversity in environments helps the speed convergence in algorithm. The proposed approaches have been tested on Moving Peaks Benchmark. The Moving Peaks Benchmark is the suitable function for testing optimization algorithms in dynamic environments. The experimental study on a Moving Peaks Benchmark show that proposed approach has a superior performance in comparison with several other algorithms in dynamic environments.