Multi-strategy collaborative improvement Dung Beetle Optimization Algorithm for Engineering Problems

Swarm intelligent algorithms have the ability to quickly find optimal solutions to problems, but they suffer from an imbalance between global exploration and local exploitation. The dung beetle optimization (DBO) algorithm was newly developed in 2022 and has excellent comprehensive performance; however, it still suffers from this problem. In this study a new multi-strategy improvement (LCTDBO) is proposed to balance the exploration and exploitation capabilities of the DBO algorithm to avoid falling into the local optimal solution. Latin hypercube sampling initializes the dung beetle to make the population initialization distribution uniform and to search the global space as much as possible. An adaptive nonlinear weight and convergence factor based on the circular arc formula are proposed to improve its global search capability, enhance local exploitation, and accelerate convergence as the number of iterations increases. Applying the t-distribution mutation strategy mutates the optimal solution of each iteration to increase the population diversity and avoid local optimal solutions. This research utilizes 23 classic benchmark functions and CEC-2022 to compare LCTDBO with six classic swarm intelligent algorithms, and three extensive practical engineering problems to verify LCTDBO's ability to solve practical problems. The results show that LCTDBO balances the global search and local exploitation well, accelerates algorithm convergence, and has excellent performance in solving practical engineering problems.