A Multi-Strategy Dung Beetle Optimization Algorithm for Optimizing Constrained Engineering Problems

• Published in: IEEE access Vol. 11; pp. 98805 - 98817
• Main Authors: Zilong, Wang, Peng, Shao
• Format: Journal Article
• Language: English
• Published: Piscataway IEEE 2023; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Algorithm design and analysis; Algorithms; Behavioral sciences; CEC2017; Convergence; Dung; Dung beetle optimization algorithm; engineering optimization; Functions (mathematics); Gbest; opposition-based learning; Optimization; Optimization methods; Rabbits; Software algorithms; Statistics
• ISSN: 2169-3536; 2169-3536
• DOI: 10.1109/ACCESS.2023.3313930

The dung beetle optimization (DBO) algorithm is one of newly excellent swarm intelligent algorithm while its exploration capability is still insufficient. For this, a multi-strategy DBO algorithm (GODBO) by utilizing the optimal value in the current population directed shift and the opposition-based learning (OBL) is proposed. In GODBO, the OBL is used to increase the likelihood of finding a better solution in the early stage of the algorithm so that the algorithm can find the optimal solution faster. Meanwhile, the current optimal value (Gbest) is used to guide the solution to search a new solution later in the algorithm, and the improved algorithm will be searched near a better solution at the later stage to get a better solution. Therefore, both are used to enhance exploration capabilities. 29 famous mathematical benchmark functions as test objects are applied to evaluate the abilities of the GODBO algorithm, and the experimental results demonstrate that GODBO performs better in the light of convergence speed and convergence accuracy in comparison with other competitors. Furthermore, two constrained engineering optimization problems are employed in GODBO to validate the effectiveness to solve practice problems, and the experiment results show that it can make tools to tackling them.