Balanced dung beetle optimization algorithm based on parameter substitution and escape strategy

Abstract

Abstract Dung Beetle algorithm is an intelligent optimization algorithm with advantages in exploitation ability. However, due to the high randomness of parameters, premature convergence and other reasons, there is an imbalance between exploration and exploitation ability, and it is easy to fall into the problem of local optimal solution. The purpose of this study is to improve the optimization performance of dung beetle algorithm and explore its engineering application value. A balanced dung beetle optimization algorithm was proposed, and parabolic adaptive parameter $$R$$ R was introduced to broaden the exploration range and slow down premature convergence. Gaussian distributed phase parameter $$\beta$$ β is introduced to reduce the randomness of parameters and stimulate the potential of algorithm exploitation. Levy flight escape strategy is introduced to balance the global exploration ability of the algorithm and fully explore the solution space. The effectiveness of the improved strategy is verified by comparing the CEC2017 benchmark function with the single strategy variant. The experimental results show that BDBO algorithm is superior to other algorithms in terms of convergence accuracy and generalization ability, and the accuracy improvement percentage is 35.29% compared with DBO algorithm. Wilcoxon rank sum test was used to evaluate the experimental results, which proved that the improvement strategy was statistically significant. Finally, the BDBO algorithm is applied to the tracking technology of the maximum power point of the photovoltaic system, and the experimental results show that the application effect of the BDBO algorithm is better and has more engineering application value.

Keywords

• Dung beetle optimization algorithm
• Swarm intelligence optimization algorithm
• Parameter substitution
• Exploitation potential
• Escape strategy
• MPPT