Affiliations: [a] Key Laboratory of Optoelectronic Technology and System of the Ministry of Education, Chongqing University, Chongqing, China
| [b] School of Mechanical and Power Engineering, Chongqing University of Science and Technology, Chongqing, China
Correspondence:
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Corresponding author. Jun Luo, Key Laboratory of Optoelectronic Technology and System of the Ministry of Education, Chongqing University, Chongqing, China. E-mail: [email protected].
Abstract: Aiming at the disadvantages of slow convergence and the premature phenomenon of the butterfly optimization algorithm (BOA), this paper proposes a modified BOA (MBOA) called reverse guidance butterfly optimization algorithm integrated with information cross-sharing. First, the quasi-opposition concept is employed in the global search phase that lacks local exploitation capabilities to broaden the search space. Second, the neighborhood search weight factor is added in the local search stage to balance exploration and exploitation. Finally, the information cross-sharing mechanism is introduced to enhance the ability of the algorithm to jump out of the local optima. The proposed MBOA is tested in fourteen benchmark functions and three constrained engineering problems. The series of experimental results indicate that MBOA shows better performance in terms of convergence speed, convergence accuracy, stability as well as robustness.
