基于过滤引导及随机性策略的差分搜索算法

Differential search algorithm based on strategies of filtration guidance and random

差分搜索算法是一种新型高效的仿生智能优化算法。但该算法仍存在收敛速度较慢,搜索精度不够高等缺点。为此,本文提出一种基于过滤引导及随机性策略的差分搜索算法。一方面,将过滤择优策略引入到搜索方程中进行首次搜索,使得算法收敛速度及搜索精度得到提高;另一方面,提出随机算子引导搜索方程,使得算法可以快速达到全局收敛。对标准测试函数进行了优化求解实验,结果表明,所提出的改进策略有效地提高了算法的优化性能,较之其它算法更适合求解复杂度高且难度较大的多模态最优化问题。

Differential search algorithm is a new and efficient bionic intelligent algorithm.However,the algorithm convergences slowly,and its search accuracy is not high enough.Therefore,the differential search algorithm based on the strategies of filtration guidance and random is proposed.On the one hand,the strategy of filtration guidance is introduced to search equation for the first search,so that the convergence speed is improved to some extent;on the other hand,random operator guides the search equation,making the algorithm achieve global convergence quickly.Through solving the standard test functions,simulation results show that the proposed algorithm has effectively improved the performance of the algorithm.Compared with other algorithms,the proposed algorithm is more suitable to solve the complex and difficult larger multi-modal optimization problem.