Xiao Wen-Sheng, Li Guang-Xin, Liu Chao, Tan Li-Ping
National Engineering Laboratory of Offshore Geophysical and Exploration Equipment, China University of Petroleum (East China), Qingdao, 266580, China.
School of Electrical and Mechanical Engineering, China University of Petroleum (East China), Qingdao, 266580, China.
Sci Rep. 2023 Nov 22;13(1):20496. doi: 10.1038/s41598-023-44770-8.
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, etc. In this study, a novel variant of ABC named chaotic and neighborhood search-based ABC algorithm (CNSABC) is proposed. The CNSABC contains three improved mechanisms, including Bernoulli chaotic mapping with mutual exclusion mechanism, neighborhood search mechanism with compression factor, and sustained bees. In detail, Bernoulli chaotic mapping with mutual exclusion mechanism is introduced to enhance the diversity and the exploration ability. To enhance the convergence efficiency and exploitation capability of the algorithm, the neighborhood search mechanism with compression factor and sustained bees are presented. Subsequently, a series of experiments are conducted to verify the effectiveness of the three presented mechanisms and the superiority of the proposed CNSABC, the results demonstrate that the proposed CNSABC has better convergence efficiency and search ability. Finally, the CNSABC is applied to solve two engineering optimization problems, experimental results show that CNSABC can produce satisfactory solutions.
随着人工智能的发展，众多研究人员致力于研究新的启发式算法并改进传统算法。人工蜂群（ABC）算法是一种受蜜蜂觅食行为启发的群体智能优化算法，是解决优化问题应用最为广泛的方法之一。然而，传统的ABC算法存在一些缺点，如开发不足和收敛速度慢等。在本研究中，提出了一种名为基于混沌和邻域搜索的ABC算法（CNSABC）的新型ABC变体算法。CNSABC算法包含三种改进机制，包括具有互斥机制的伯努利混沌映射、具有压缩因子的邻域搜索机制和持续蜜蜂机制。具体而言，引入具有互斥机制的伯努利混沌映射以增强多样性和探索能力。为提高算法的收敛效率和开发能力，提出了具有压缩因子的邻域搜索机制和持续蜜蜂机制。随后，进行了一系列实验以验证所提出的三种机制的有效性以及所提CNSABC算法的优越性，结果表明所提CNSABC算法具有更好的收敛效率和搜索能力。最后，将CNSABC算法应用于解决两个工程优化问题，实验结果表明CNSABC算法能够产生令人满意的解。
