基于Logistic模型和随机差分变异的正弦余弦算法

针对标准正弦余弦算法(Sine Cosine Algorithm,SCA)处理全局优化问题时存在收敛速度慢、易陷入局部最优和求解精度低的缺点,文中提出了一种基于非线性转换参数和随机差分变异策略的改进正弦余弦算法(LS-SCA)。首先,设计一种基于Logistic模型的非线性转换参数策略以平衡算法的全局搜索和局部开发能力;其次,引入随机差分变异策略以增强种群的多样性与避免算法陷入局部最优;最后,将非线性转换参数和随机差分变异策略进行融合。一方面,选取12个标准测试函数进行全局寻优的仿真实验。结果表明,与其他SCA类算法和最新智能算法相比,LS-SCA在收敛精度和收敛速度指标上均能达到较优的效果。其中,随机差分变异策略对LS-SCA全局寻优能力的提升尤为明显。另一方面,利用LS-SCA优化神经网络参数解决了两类经典分类问题。实验结果表明,与传统的BP算法和其他智能算法相比,基于LS-SCA的神经网络能达到较高的分类准确率。

Sine Cosine Algorithm Based on Logistic Model and Stochastic Differential Mutation

In view of the slow convergence speed,easy to fall into local optimum and low precision of the standard sine cosine algorithm,an improved sine cosine algorithm(LS-SCA)with the nonlinear conversion parameter and the stochastic differential mutation strategy was proposed to solve global optimization problems.Firstly,a nonlinear conversion parameter based on Logistic model is designed to balance between global exploration and local exploitation.Secondly,a stochastic differential mutation strate-gy is introduced to maintain the diversity of population and avoid falling into the optimal value.Finally,the nonlinear conversion parameter and stochastic differential mutation strategies are fused.On the one hand,12 standard test functions are selected for global optimization experiments.The results show that LS-SCA is superior to the other SCAs and comparison latest algorithms in convergence accuracy and convergence speed with the same number of fitness function evaluations.Stochastic differential mutation strategy can improve LS-SCA’s global optimization ability especially.On the other hand,LS-SCA is used to optimize the parameters of neural network to solve two classical classification problems.Compared with the traditional BP algorithm and the other intelligent algorithms,the neural network based on LS-SCA can achieve higher classification accuracy.