An Enhanced Dung Beetle Optimization Algorithm for Global Optimization

This paper proposes an enhanced dung beetle optimization (EDBO) algorithm in order to address the issues of (DBO) which include easy convergence local optimal, slow speed, and poor global search capability. The improvements EDBO are implemented via following four aspects. Firstly, SPM chaotic mapping designed through combing Sine Piece-Wise Linear Chaotic Mapping is introduced initialize population for increasing diversity population. Secondly, position update formula Golden Algorithm (Golden-SA) used replace mathematical model ball-rolling behavior without obstacle with purpose improving accuracy accelerating speed. Thirdly, spiral foraging strategy tuna swam (TSO) hybridized breeding behavior. hybridization not only balances exploration exploitation but also keeps Finally, can enhance capability escaping optima extending space by means bringing two different sets adaptive weight coefficients. performance evaluated compared other swarm intelligence algorithms benchmark functions characteristics. results demonstrate that outperforms classical DBO terms speed accuracy.