Explicit memory based ABC with a clustering strategy for updating and retrieval of memory in dynamic environments

Abstract

The Artificial Bee Colony (ABC) algorithm is considered as one of the swarm intelligence optimization algorithms. It has been extensively used for the applications of static type. Many practical and real-world applications, nevertheless, are of dynamic type. Thus, it is needed to employ some optimization algorithms that could solve this group of the problems that are of dynamic type. Dynamic optimization problems in which change(s) may occur through the time are tougher to face than static optimization problems. In this paper, an approach based on the ABC algorithm enriched with explicit memory and population clustering scheme, for solving dynamic optimization problems is proposed. The proposed algorithm uses the explicit memory to store the aging best solutions and employs clustering for preserving diversity in the population. Using the aging best solutions and keeping the diversity in population of the candidate solutions in the environment help speed-up the convergence of the algorithm. The proposed approach has been tested on Moving Peaks Benchmark. The Moving Peaks Benchmark is a suitable function for testing optimization algorithms and it is considered as one of the best representative of dynamic environments. The experimental study on the Moving Peaks Benchmark shows that the proposed approach is superior to several other well-known and state-of-the-art algorithms in dynamic environments.