CAJ | 학술논문

　　针对现有的粒子群优化（PSO）算法大多存在早熟收敛、容易陷入局部最优值的问题，提出了一种新的协同粒子群优化（CPSO）算法。该算法拥有两个子群，一个用于全局搜索始终保持粒子多样性，另一个用于局部搜索保证搜索精度，通过相互协同合作在全局最优值附近实现精确搜索。最后把该算法应用到动态聚类入侵检测，通过优化聚类半径和聚类阈值，对训练数据进行正、异常类聚类，然后用测试数据进行攻击检测。试验结果表明该算法较粒子群和突变粒子群（MPSO）算法性能明显提高。
　　침대현유적입자군우화（PSO）산법대다존재조숙수렴、용역함입국부최우치적문제，제출료일충신적협동입자군우화（CPSO）산법。해산법옹유량개자군，일개용우전국수색시종보지입자다양성，령일개용우국부수색보증수색정도，통과상호협동합작재전국최우치부근실현정학수색。최후파해산법응용도동태취류입침검측，통과우화취류반경화취류역치，대훈련수거진행정、이상류취류，연후용측시수거진행공격검측。시험결과표명해산법교입자군화돌변입자군（MPSO）산법성능명현제고。
Aiming at the problems of premature convergence and easy to fall into local optimum value of existing particle swarm optimization (PSO) algorithms, a new collaborative particle swarm optimization (CPSO) algorithm is proposed. CPSO algorithm has two subgroups, one subgroup is used for global search always keep particle diversity, the other one is used for local search guarantee search precision. So precise search is realized nearly the global optimal value by mutual cooperation. Finally, the proposed algorithm applied to intrusion detection based on dynamic cluster. Through the optimization of clustering radius and clustering threshold , the training data is classified as normal and abnormal clustering. Then test data is used to attack detection. The results show that CPSO algorithm has a marked improvement in performance over the traditional PSO algorithm and improved mutation particle swarm (MPSO) algorithm.