An opposition-based butterfly optimization algorithm with adaptive elite mutation in solving complex high-dimensional optimization problems

To solve complex high-dimensional optimization problems, an opposition-based butterfly optimization algorithm with adaptive elite mutation (OBOAEM) is proposed. In the initial stage, the opposition-based learning mechanism is introduced to increase the diversity of the initial population and improve the probability of finding the optimal value. In order to balance the process of global search and local search, the segmental adjustment factor is used to improve the optimization accuracy of the algorithm. In the final stage of the algorithm, the elite mutation strategy is adopted to prevent precocity of the algorithm. In this paper, 23 benchmark functions are selected to test OBOAEM and original algorithm BOA in low dimension, and 16 benchmark functions are introduced to test OBOAEM and eight intelligent optimization algorithms in high dimensions 100, 500 and 1000. In addition, the simulation experiments of 30 CEC2014 complex deformation functions reveal the OBOAEM has a good effect in solving complex optimization problems, which are compared with six state-of-art algorithms. Friedman test is used forstatistical analysis, showing that OBOAEM has better optimization performance for complex high-dimensional problems. Finally, OBOAEM is applied to engineering design problems, and it is proved that OBOAEM is competitive in solving real-world problems. (c) 2022 International Association for Mathematics and Computers in Simulation (IMACS). Published by Elsevier B.V. All rights reserved.

Automated summary

This paper proposes an opposition-based butterfly optimization algorithm with adaptive elite mutation (OBOAEM) to solve complex high-dimensional optimization problems. Through testing with benchmark functions and complex deformation functions, the effectiveness and competitiveness of the algorithm in solving real-world problems and optimization performance are demonstrated.