Coyote Optimization Algorithm (COA) is classified as both swarm intelligence and evolutionary heuristic algorithms. However, getting trapped in a poor local optimum and the low convergence speed are the weaknesses of COA obviously. Due to these weaknesses, this paper proposes a new algorithm named Chaotic Coyote Optimization Algorithm (CCOA) which focusing on COA equipped with chaotic maps. Through utilising ten well-known benchmark functions, experimental results are recorded in tables and drawn in figures to provide a sharp contrast. The performance of CCOA and COA are discussed, which proves CCOA outperforms COA guaranteeing rapid global convergence rate.

土狼优化算法（COA）被归类为群体智能算法和进化启发式算法。然而，陷于较差的局部最优值和较低的收敛速度显然是COA的弱点。由于这些弱点，本文提出了一种新的算法，称为混沌土狼优化算法（CCOA），该算法着重于装备有混沌图的COA。通过利用十项众所周知的基准功能，实验结果记录在表格中并绘制在图中，以提供鲜明的对比。讨论了CCOA和COA的性能，证明CCOA优于保证快速全球收敛速度的COA。