基于三重约束社会蜘蛛优化的最小属性约简算法

Minimum Attribute Reduction Algorithm Based on Triple Restraints Social Spider Optimization

针对社会蜘蛛优化算法求解最小属性约简时收敛速度慢和约简结果差的问题,提出一种基于三重约束的社会蜘蛛优化最小属性约简算法(TRSSOAR),分别对初始化阶段、迭代过程中以及迭代结束后种群中的个体进行约束。首先,提出一种适应度投票策略优化种群初始状态,使种群中的多数个体处于良好的位置;然后,在迭代过程中引入对立学习,设计局部对立学习策略提升种群个体质量,扩大搜索空间;接下来为了获得较少的约简结果,采用冗余检测策略去除约简结果中的冗余属性;最后,在9个UCI数据集上进行实验,并与4种代表性算法进行比较。结果表明,该算法在约简能力、运行时间和收敛速度上均表现良好,在求解最小属性约简问题上具有一定的优越性。

Aiming at the problem of slow convergence speed and poor reduction results when the social spider optimization algorithm solves the minimum attribute reduction.This paper proposed a minimum attribute reduction algorithm based on triple restraints social spider optimization(TRSSOAR).Constrain the individuals in the population during the initialization stage,during the iteration process and at the end of the iteration respectively.First,a fitness voting strategy is proposed to optimize the initial state of the population so that most individuals in the population are in a good position;Then,in the iterative process,opposition-based learning is introduced,and a local opposition-based learning strategy is designed to improve the individual quality of the population and expand the search space;Thirdly,in order to obtain fewer reduction results,a redundancy detection strategy is used to remove redundant attributes in the reduction results;finally,experiments are conducted on nine UCI data sets and compared with four representative algorithms.The results show that the proposed algorithm performs well in terms of reduction capability,running time and convergence speed,and has certain advantages in solving the minimum attribute reduction problem.