双策略学习和自适应混沌变异的郊狼优化算法

针对郊狼优化算法（coyote optimization algorithm，COA）存在收敛速度慢、求解精度低、易陷入局部最优的不足，提出一种基于双策略学习机制和自适应混沌变异策略的改进郊狼算法（coyote optimization algorithm based on dual strategy learning and adaptive chaotic mutation，DCSCOA）。首先，引入振荡递减因子，以产生具有多样性的个体来增强全局搜索能力；其次，利用双策略学习机制，适度地增强组群头狼的影响，以平衡算法的局部挖掘能力和全局搜索能力，同时提高算法的求解精度和收敛速度；最后，使用自适应混沌变异机制，在算法停滞时产生新个体，以使算法跳出局部最优。通过对20个基本测试函数和11个CEC2017测试函数进行仿真实验，结果验证了改进算法具有更高的求解精度、更快的收敛速度和更强的稳定性。

Coyote optimization algorithm based on dual strategy learning and adaptive chaotic mutation

Aiming at the shortcomings of COA, such as slow convergence speed, low solution accuracy and being easy to fall into local optimum, this paper proposed an improved coyote optimization algorithm based on dual strategy learning mechanism and adaptive chaotic mutation strategy(DCSCOA). Firstly, it adopted an oscillatory decline factor to generate diverse individuals for enhancing the global search ability. Secondly, it proposed a dual strategy learning mechanism to appropriately increase the influence of the group head wolf, so as to balance the local search ability and global search ability of the algorithm, and to improve the solution accuracy and convergence speed of the algorithm. Finally, it used an adaptive chaotic mutation mechanism to generate new individuals when the algorithm stagnates, so as to make the algorithm jump out of the local optimum. Through simulation experiments on 20 basic test functions and 11 CEC2017 test functions, the results show that the improved algorithm has higher solution accuracy, faster convergence speed and stronger stability.