A Novel Chaotic and Neighborhood Search-based Artificial Bee Colony algorithm for Solving Optimization Problems

Abstract

Abstract With the development of artificial intelligence, numerous researchers are attracted to study new heuristic algorithms and improve traditional algorithms. Artificial bee colony (ABC) algorithm is a swarm intelligence optimization algorithm inspired by the foraging behavior of honeybees, which is one of the most widely applied methods to solve optimization problems. However, the traditional ABC has some shortcomings such as under-exploitation and slow convergence. In this study, a novel variant of ABC named chaotic and neighborhood search-based ABC algorithm (CNSABC) is proposed. The CNSABC contains main three improved mechanisms including Bernoulli chaotic mapping with mutual exclusion mechanism, new neighborhood search mechanism and sustained bees. In detail, Bernoulli chaotic mapping with mutual exclusion mechanism is introduced to enhance the diversity and traversal of initial nectar sources and scout bees to find nectar sources, further to improve the exploration ability of peripatetic bees. A new neighborhood search mechanism and sustained bees are proposed to enhance the convergence efficiency and local exploitation capability of the algorithm. Subsequently, a series of experiments are conducted to verify the effectiveness of the three presented mechanisms and the superiority of the proposed CNSABC algorithm. Compared with 8 existing approaches for testing 25 typical benchmark functions, including eight variants of ABC (ABC, CABC, NABC, qABC, SBABC, MPGABC, GABC and NGABC), and five other original basic algorithms (PSO, ABC, GWO, WOA and BOA), the results demonstrate that the proposed CNSABC has better convergence and search ability. Finally, the CNSABC is applied to solve two engineering optimization problems, experimental results show that CNSABC can produce satisfactory solutions.