Treffer: Improved dung beetle optimization algorithm based on multi-strategy collaborative mechanisms for global optimization and engineering optimization design problems.

Aiming at the problems of slow convergence and easy to fall into local optimum of dung beetle optimization algorithm (DBO) this paper proposes an improved dung beetle optimization algorithm (MDBO) based on multi-strategy collaborative mechanism. First, a random center of gravity reverse chaos initialization strategy is used in the initialization stage to enhance the population diversity; second, a multi-strategy cooperative mechanism is designed to adaptively select some particles for position swapping to better explore the whole search space, prevent premature convergence, and construct the search strategy of each part to improve the convergence accuracy of the algorithm and enhance the ability to jump out of the local optimum; lastly, a mirror-reflective boundary processing strategy so that individuals exceeding the search boundary are effectively bounced back inside the boundary to enhance the stability of the algorithm. In this paper, we simulate and analyze some of the CEC2011, CEC2017, CEC2020 and CEC2022 test functions sets, and compare MDBO with other optimization algorithms. The performance of the MDBO algorithm is evaluated by ANOVA, Wilcoxon rank sum test and Friedman test. The experimental results show that the MDBO algorithm proposed in this paper has been significantly improved in terms of convergence accuracy and speed, among a total of 51 benchmark test functions, the proposed MDBO algorithm achieved better average performance in 47 groups, which accounts for approximately 92.16%, demonstrating its superiority and reliability in solving optimization problems. Meanwhile, the application of the MDBO algorithm to engineering optimization design problems further confirms its significant advantages in solving practical engineering problems. [ABSTRACT FROM AUTHOR]

Copyright of Cluster Computing is the property of Springer Nature and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)