针对当前网络入侵检测中的数据量较大、数据维度较高的特点,将飞蛾扑火优化(MFO)算法应用于网络入侵检测的特征选择中。鉴于MFO算法收敛过快、易陷入局部最优的问题,提出一种融合粒子群优化(PSO)的二进制飞蛾扑火优化(BPMFO)算法。该算...针对当前网络入侵检测中的数据量较大、数据维度较高的特点,将飞蛾扑火优化(MFO)算法应用于网络入侵检测的特征选择中。鉴于MFO算法收敛过快、易陷入局部最优的问题,提出一种融合粒子群优化(PSO)的二进制飞蛾扑火优化(BPMFO)算法。该算法引入MFO螺旋飞行公式,具有较强的局部搜索能力;结合了粒子群优化(PSO)算法的速度更新方法,让种群个体随着全局最优解和历史最优解的方向移动,增强算法的全局收敛性,从而避免易陷入局部最优。仿真实验以KDD CUP 99数据集为实验基础,分别采用支持向量机(SVM)、K最近邻(KNN)算法和朴素贝叶斯(NBC)3种分类器,与二进制飞蛾扑火优化(BMFO)算法、二进制粒子群优化(BPSO)算法、二进制遗传算法(BGA)、二进制灰狼优化(BGWO)算法和二进制布谷鸟搜索(BCS)算法进行了实验对比。实验结果表明,BPMFO算法应用于网络入侵检测的特征选择时,在算法精度、运行效率、稳定性、收敛速度以及跳出局部最优的综合性能上具有明显优势。展开更多
针对大电网低频振荡现象存在机理分析复杂、振荡模式多样、参与机组众多、传统电力系统稳定器(power system stabilizer,PSS)整定方法适应性较差的问题,提出了一种基于飞蛾扑火优化(moth-flame optimization,MFO)算法的多运行方式PSS参...针对大电网低频振荡现象存在机理分析复杂、振荡模式多样、参与机组众多、传统电力系统稳定器(power system stabilizer,PSS)整定方法适应性较差的问题,提出了一种基于飞蛾扑火优化(moth-flame optimization,MFO)算法的多运行方式PSS参数协调优化方法。该方法首先基于主导振荡模式及动态响应因子提取主要参与机组;然后考虑PSS临界增益及相频特性补偿范围约束,以PSS参数鲁棒性及系统动态稳定性为目标函数;最后采用MATLAB与PSD-BPA联合仿真方法,建立基于MFO算法的多运行方式PSS参数协调优化算法,完成大电网的全局参数寻优。华中电网仿真算例结果表明,应用文中方法优化后的PSS参数可有效提高系统动态稳定性,且对多种运行方式均有较好的适应性,同时算法本身具有较强的收敛性。展开更多
Moth Flame Optimization(MFO)is a nature-inspired optimization algorithm,based on the principle of navigation technique of moth toward moon.Due to less parameter and easy implementation,MFO is used in various field to ...Moth Flame Optimization(MFO)is a nature-inspired optimization algorithm,based on the principle of navigation technique of moth toward moon.Due to less parameter and easy implementation,MFO is used in various field to solve optimization problems.Further,for the complex higher dimensional problems,MFO is unable to make a good trade-off between global and local search.To overcome these drawbacks of MFO,in this work,an enhanced MFO,namely WF-MFO,is introduced to solve higher dimensional optimization problems.For a more optimal balance between global and local search,the original MFO’s exploration ability is improved by an exploration operator,namely,Weibull flight distribution.In addition,the local optimal solutions have been avoided and the convergence speed has been increased using a Fibonacci search process-based technique that improves the quality of the solutions found.Twenty-nine benchmark functions of varying complexity with 1000 and 2000 dimensions have been utilized to verify the projected WF-MFO.Numerous popular algorithms and MFO versions have been compared to the achieved results.In addition,the robustness of the proposed WF-MFO method has been evaluated using the Friedman rank test,the Wilcoxon rank test,and convergence analysis.Compared to other methods,the proposed WF-MFO algorithm provides higher quality solutions and converges more quickly,as shown by the experiments.Furthermore,the proposed WF-MFO has been used to the solution of two engineering design issues,with striking success.The improved performance of the proposed WF-MFO algorithm for addressing larger dimensional optimization problems is guaranteed by analyses of numerical data,statistical tests,and convergence performance.展开更多
文摘针对当前网络入侵检测中的数据量较大、数据维度较高的特点,将飞蛾扑火优化(MFO)算法应用于网络入侵检测的特征选择中。鉴于MFO算法收敛过快、易陷入局部最优的问题,提出一种融合粒子群优化(PSO)的二进制飞蛾扑火优化(BPMFO)算法。该算法引入MFO螺旋飞行公式,具有较强的局部搜索能力;结合了粒子群优化(PSO)算法的速度更新方法,让种群个体随着全局最优解和历史最优解的方向移动,增强算法的全局收敛性,从而避免易陷入局部最优。仿真实验以KDD CUP 99数据集为实验基础,分别采用支持向量机(SVM)、K最近邻(KNN)算法和朴素贝叶斯(NBC)3种分类器,与二进制飞蛾扑火优化(BMFO)算法、二进制粒子群优化(BPSO)算法、二进制遗传算法(BGA)、二进制灰狼优化(BGWO)算法和二进制布谷鸟搜索(BCS)算法进行了实验对比。实验结果表明,BPMFO算法应用于网络入侵检测的特征选择时,在算法精度、运行效率、稳定性、收敛速度以及跳出局部最优的综合性能上具有明显优势。
文摘针对大电网低频振荡现象存在机理分析复杂、振荡模式多样、参与机组众多、传统电力系统稳定器(power system stabilizer,PSS)整定方法适应性较差的问题,提出了一种基于飞蛾扑火优化(moth-flame optimization,MFO)算法的多运行方式PSS参数协调优化方法。该方法首先基于主导振荡模式及动态响应因子提取主要参与机组;然后考虑PSS临界增益及相频特性补偿范围约束,以PSS参数鲁棒性及系统动态稳定性为目标函数;最后采用MATLAB与PSD-BPA联合仿真方法,建立基于MFO算法的多运行方式PSS参数协调优化算法,完成大电网的全局参数寻优。华中电网仿真算例结果表明,应用文中方法优化后的PSS参数可有效提高系统动态稳定性,且对多种运行方式均有较好的适应性,同时算法本身具有较强的收敛性。
文摘Moth Flame Optimization(MFO)is a nature-inspired optimization algorithm,based on the principle of navigation technique of moth toward moon.Due to less parameter and easy implementation,MFO is used in various field to solve optimization problems.Further,for the complex higher dimensional problems,MFO is unable to make a good trade-off between global and local search.To overcome these drawbacks of MFO,in this work,an enhanced MFO,namely WF-MFO,is introduced to solve higher dimensional optimization problems.For a more optimal balance between global and local search,the original MFO’s exploration ability is improved by an exploration operator,namely,Weibull flight distribution.In addition,the local optimal solutions have been avoided and the convergence speed has been increased using a Fibonacci search process-based technique that improves the quality of the solutions found.Twenty-nine benchmark functions of varying complexity with 1000 and 2000 dimensions have been utilized to verify the projected WF-MFO.Numerous popular algorithms and MFO versions have been compared to the achieved results.In addition,the robustness of the proposed WF-MFO method has been evaluated using the Friedman rank test,the Wilcoxon rank test,and convergence analysis.Compared to other methods,the proposed WF-MFO algorithm provides higher quality solutions and converges more quickly,as shown by the experiments.Furthermore,the proposed WF-MFO has been used to the solution of two engineering design issues,with striking success.The improved performance of the proposed WF-MFO algorithm for addressing larger dimensional optimization problems is guaranteed by analyses of numerical data,statistical tests,and convergence performance.
