基于动态分级策略的改进正余弦算法

针对正余弦算法存在易陷入局部最优、求解精度不高、收敛速度较慢等问题, 提出一种基于动态分级策略的改进正余弦算法。首先, 引入拉丁超立方抽样法, 将搜索空间均匀划分, 使初始种群覆盖整个搜索空间, 以保持初始种群的多样性。其次, 采用动态分级策略, 根据适应度值的排序情况, 将种群动态划分为好中差3个等级, 并应用破坏策略与精英引导方法对其进行扰动, 以提高算法的收敛精度, 增强跳出局部最优的能力。最后, 引入反向学习方法, 设计了动态反向学习全局搜索策略, 以提高算法的收敛速度，同时对改进算法在复杂度、收敛性和稳定性方面进行性能测试, 选取15个标准测试函数在低维和高维状态下进行仿真实验分析, 并与粒子群算法、回溯搜索算法和其他改进正余弦算法进行比较。仿真分析结果表明, 所提算法有效地提高了算法的收敛性和稳定性。

Improved sine cosine algorithm based on dynamic classification strategy

Aiming at the problems of sine cosine algorithm, such as easy to fall into local optimum, low accuracy and slow convergence speed, an improved sine cosine algorithm based on dynamic classification strategy is proposed. Firstly, the Latin hypercube sampling method is introduced to divide the search space evenly so that the initial population covers the whole search space to maintain the diversity of the initial population. Secondly, the dynamic classification strategy is used, according to the order of fitness value, the population dynamics is divided into three grades: good, medium and poor. The destruction strategy and elite guidance method are used to disturb the population dynamics, so as to improve the convergence accuracy of the algorithm and enhance the ability to jump out of the local optimum. Finally, the global search strategy of dynamic reverse learning is designed by introducing the reverse learning method to improve the convergence speed of the algorithm. At the same time, the performance of the improved algorithm in the aspects of complexity, convergence and stability is tested, and 15 standard test functions are selected to carry out the simulation experiment analysis in the low and high dimensional state, and compared with particle swarm optimization algorithm, backtracking search algorithm and other improved sine cosine algorithms. The simulation results show that the proposed algorithm can improve the convergence and stability of the algorithm.