结合柯西分布和高斯扰动的正余弦混合优化算法

Sine-Cosine Hybrid Algorithm Combining Cauchy Distribution and Gaussian Disturbance

针对正余弦优化算法(SCA)存在的寻优精度低、收敛速度慢、易陷入局部最优等缺陷,提出一种结合柯西分布和高斯扰动的正余弦混合优化算法。利用Cubic混沌映射提高初始种群的个体质量;构建自适应调整的能量因子来平衡算法的全局探索和局部开发能力;通过柯西分布的逆累积分布函数方法更新个体位置,加强算法的全局探索能力;混合算法根据策略选择因子有条件地执行SCA算法和高斯扰动策略,增加迭代后期种群多样性,降低算法陷入局部极值的概率,提升算法的寻优精度和收敛速度。通过10个经典测试函数的低维与高维验证,所提算法相较于对比算法,具有更高的寻优精度和更快的收敛速度。

Aiming at the shortcomings of sine cosine algorithm(SCA),such as low optimization accuracy,slow convergence speed,and easy to fall into local optimal value,a hybrid sine-cosine algorithm combining Cauchy distribution and Gaussian disturbance was proposed.Cubic chaotic mapping was used to initialize the population of the algorithm to improve the individual quality of the population.An adaptive energy factor was constructed to balance the ability of global exploration and local exploitation of the algorithm.The inverse cumulative distribution function method of Cauchy distribution was used to update individual position and strengthen the global exploration capability of the algorithm.The hybrid algorithm executes SCA algorithm and Gaussian perturbation strategy conditionally according to the strategy selection factor,increases the population diversity in the late iteration,reduces the probability of the algorithm falling into the local extreme value,and improves the optimization accuracy and convergence speed of the algorithm.Through the verification of 10 classical test functions in low and high dimensions,the proposed algorithm has higher optimization accuracy and faster convergence speed than the comparison algorithm.