An Adaptive Dung Beetle Optimization Algorithm with Golden Sine for Optimizing Numerical Unconstrained Problems

Abstract

The dung beetle optimization (DBO) algorithm is a newly swarm intelligence optimization algorithm inspired by the biological behaviors of dung beetles while it still has disadvantages of easy convergence to the local optimal, slow convergence speed, and poor global search capability. This paper proposes an adaptive dung beetle optimization algorithm with a golden sine algorithm (Gold-SA), denoted as the Gold-SA-based adaptive DBO (GSDBO) algorithm. Firstly, the PWLCM chaotic mapping is introduced to generate population individuals to increase diversity of population and explore more search space. Secondly, the position update formula for the mathematical model of dung beetle ball-rolling behavior without obstacle is replaced by that of Gold-SA, which can accelerate the convergence speed and improve the convergence accuracy. Finally, the adaptive weight coefficients are used to improve the update stage of thief beetles. The strategy can boost and balance the exploration vs exploitation, simultaneously. Furthermore, the GSDBO is proved to be effective by comparing some intelligence optimization algorithms on benchmark functions of different characteristics. The results demonstrate that the GSDBO can improve optimization accuracy and stability.