自学习策略和Lévy飞行的正弦余弦优化算法

针对正弦余弦算法（SCA，sine cosine algorithm）局部搜索能力差的缺陷，提出自学习策略和Lévy飞行的正弦余弦优化算法（SCASL，sine cosine optimization algorithm with self-learning strategy and Lévy flight）。首先，提出正弦余弦算法自学习策略和非线性权重因子，使搜索个体记忆自身历史最优位置，在寻优过程中指导搜索个体更新位置，提高SCA的局部搜索能力；算法寻优后期，当搜索陷入局部最优时，采用基于Lévy飞行的停滞扰动策略使算法跳出局部最优，提高SCA的局部最优规避能力。基于13个经典基准测试函数对算法性能进行测试的实验结果表明，SCASL相比标准SCA和较新的优化算法SSA，VCS，WOA，GSA，具有更高的计算效率，收敛精度以及更强的局部最优规避能力。求解无人作战飞机航迹规划的仿真结果表明，在有6个敌方威胁源的战场环境中，相比SCA，SCASL求解得到的飞行航迹具有更小的航迹代价。综上，所提出的SCASL具有较强的寻优能力。

An improved sine cosine optimization algorithm with self-learning strategy and Lévy flight

In order to improve the performance of sine cosine algorithm (SCA) with poor local search ability, an sine cosine optimization algorithm with self-learning strategy and Lévy flight (SCASL) was proposed. Firstly, the self-learning strategy and nonlinear weight factor of sine cosine algorithm was proposed, so that the search individual could remember its historical optimal position, which guided the individual to update its location in the optimization process, thus improving the local search ability of SCA. When the search was stagnant, the stagnation perturbation strategy based on Lévy flight was adopted to jump out of local optimum so as to improve the local optimum avoidance ability. Based on 13 classic benchmark functions, the numerical simulation was conducted and the results show that SCASL has higher computational efficiency, convergence accuracy and stronger local optimum avoidance ability compared with standard SCA and other state-of-the-art optimization algorithms such as SSA,VCS,WOA and GSA. The simulation results of unmanned combat aircraft flight path plan show that for the battlefield environment with six enemy threat sources, SCASL can stably obtain less expensive flight path than the standard SCA.Therefore, the proposed SCASL has better optimization performance.