Moth-flame optimization(MFO)is a novel metaheuristic algorithm inspired by the characteristics of a moth’s navigation method in nature called transverse orientation.Like other metaheuristic algorithms,it is easy to f...Moth-flame optimization(MFO)is a novel metaheuristic algorithm inspired by the characteristics of a moth’s navigation method in nature called transverse orientation.Like other metaheuristic algorithms,it is easy to fall into local optimum and leads to slow convergence speed.The chaotic map is one of the best methods to improve exploration and exploitation of the metaheuristic algorithms.In the present study,we propose a chaos-enhanced MFO(CMFO)by incorporating chaos maps into the MFO algorithm to enhance its performance.The chaotic map is utilized to initialize the moths’population,handle the boundary overstepping,and tune the distance parameter.The CMFO is benchmarked on three groups of benchmark functions to find out the most efficient one.The performance of the CMFO is also verified by using two real engineering problems.The statistical results clearly demonstrate that the appropriate chaotic map(singer map)embedded in the appropriate component of MFO can significantly improve the performance of MFO.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
The purpose of this study was to grasp current potential problems of dose error in intensity-modulated proton therapy (IMPT) plans. We were interested in dose differences of the Varian Eclipse treatment planning syste...The purpose of this study was to grasp current potential problems of dose error in intensity-modulated proton therapy (IMPT) plans. We were interested in dose differences of the Varian Eclipse treatment planning system (TPS) and the fast dose calculation method (FDC) for single-field optimization (SFO) and multi-field optimization (MFO) IMPT plans. In addition, because some authors have reported dosimetric benefit of a proton arc therapy with ultimate multi-fields in recent years, we wanted to evaluate how the number of fields and beam angles affect the differences for IMPT plans. Therefore, for one brain cancer patient with a large heterogeneity, SFO and MFO IMPT plans with various multi-angle beams were planned by the TPS. Dose distributions for each IMPT plan were calculated by both the TPS’s conventional pencil beam algorithm and the FDC. The dosimetric parameters were compared between the two algorithms. The TPS overestimated 400 - 500 cGy (RBE) for minimum dose to the CTV relative to the dose calculated by the FDC. These differences indicate clinically relevant effect on clinical results. In addition, we observed that the maximum difference in dose calculated between the TPS and the FDC was about 900 cGy (RBE) for the right optic nerve, and this quantity also has a possibility to have a clinical effect. The major difference was not seen in calculations for SFO IMPT planning and those for MFO IMPT planning. Differences between the TPS and the FDC in SFO and MFO IMPT plans depend strongly on beam arrangement and the presence of a heterogeneous body. We advocate use of a Monte Carlo method in proton treatment planning to deliver the most precise proton dose in IMPT.展开更多
In the original Moth-Flame Optimization(MFO),the search behavior of the moth depends on the corresponding flame and the interaction between the moth and its corresponding flame,so it will get stuck in the local optimu...In the original Moth-Flame Optimization(MFO),the search behavior of the moth depends on the corresponding flame and the interaction between the moth and its corresponding flame,so it will get stuck in the local optimum easily when facing the multi-dimensional and high-dimensional optimization problems.Therefore,in this work,a generalized oppositional MFO with crossover strategy,named GCMFO,is presented to overcome the mentioned defects.In the proposed GCMFO,GOBL is employed to increase the population diversity and expand the search range in the initialization and iteration jump phase based on the jump rate;crisscross search(CC)is adopted to promote the exploitation and/or exploration ability of MFO.The proposed algorithm’s performance is estimated by organizing a series of experiments;firstly,the CEC2017 benchmark set is adopted to evaluate the performance of GCMFO in tackling high-dimensional and multimodal problems.Secondly,GCMFO is applied to handle multilevel thresholding image segmentation problems.At last,GCMFO is integrated into kernel extreme learning machine classifier to deal with three medical diagnosis cases,including the appendicitis diagnosis,overweight statuses diagnosis,and thyroid cancer diagnosis.Experimental results and discussions show that the proposed approach outperforms the original MFO and other state-of-the-art algorithms on both convergence speed and accuracy.It also indicates that the presented GCMFO has a promising potential for application.展开更多
The software engineering technique makes it possible to create high-quality software.One of the most significant qualities of good software is that it is devoid of bugs.One of the most time-consuming and costly softwar...The software engineering technique makes it possible to create high-quality software.One of the most significant qualities of good software is that it is devoid of bugs.One of the most time-consuming and costly software proce-dures isfinding andfixing bugs.Although it is impossible to eradicate all bugs,it is feasible to reduce the number of bugs and their negative effects.To broaden the scope of bug prediction techniques and increase software quality,numerous causes of software problems must be identified,and successful bug prediction models must be implemented.This study employs a hybrid of Faster Convolution Neural Network and the Moth Flame Optimization(MFO)algorithm to forecast the number of bugs in software based on the program data itself,such as the line quantity in codes,methods characteristics,and other essential software aspects.Here,the MFO method is used to train the neural network to identify optimal weights.The proposed MFO-FCNN technique is compared with existing methods such as AdaBoost(AB),Random Forest(RF),K-Nearest Neighbour(KNN),K-Means Clustering(KMC),Support Vector Machine(SVM)and Bagging Clas-sifier(BC)are examples of machine learning(ML)techniques.The assessment method revealed that machine learning techniques may be employed successfully and through a high level of accuracy.The obtained data revealed that the proposed strategy outperforms the traditional approach.展开更多
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
基金supported by the Military Science Project of the National Social Science Foundation of China(15GJ003-141)
文摘Moth-flame optimization(MFO)is a novel metaheuristic algorithm inspired by the characteristics of a moth’s navigation method in nature called transverse orientation.Like other metaheuristic algorithms,it is easy to fall into local optimum and leads to slow convergence speed.The chaotic map is one of the best methods to improve exploration and exploitation of the metaheuristic algorithms.In the present study,we propose a chaos-enhanced MFO(CMFO)by incorporating chaos maps into the MFO algorithm to enhance its performance.The chaotic map is utilized to initialize the moths’population,handle the boundary overstepping,and tune the distance parameter.The CMFO is benchmarked on three groups of benchmark functions to find out the most efficient one.The performance of the CMFO is also verified by using two real engineering problems.The statistical results clearly demonstrate that the appropriate chaotic map(singer map)embedded in the appropriate component of MFO can significantly improve the performance of MFO.
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.
文摘The purpose of this study was to grasp current potential problems of dose error in intensity-modulated proton therapy (IMPT) plans. We were interested in dose differences of the Varian Eclipse treatment planning system (TPS) and the fast dose calculation method (FDC) for single-field optimization (SFO) and multi-field optimization (MFO) IMPT plans. In addition, because some authors have reported dosimetric benefit of a proton arc therapy with ultimate multi-fields in recent years, we wanted to evaluate how the number of fields and beam angles affect the differences for IMPT plans. Therefore, for one brain cancer patient with a large heterogeneity, SFO and MFO IMPT plans with various multi-angle beams were planned by the TPS. Dose distributions for each IMPT plan were calculated by both the TPS’s conventional pencil beam algorithm and the FDC. The dosimetric parameters were compared between the two algorithms. The TPS overestimated 400 - 500 cGy (RBE) for minimum dose to the CTV relative to the dose calculated by the FDC. These differences indicate clinically relevant effect on clinical results. In addition, we observed that the maximum difference in dose calculated between the TPS and the FDC was about 900 cGy (RBE) for the right optic nerve, and this quantity also has a possibility to have a clinical effect. The major difference was not seen in calculations for SFO IMPT planning and those for MFO IMPT planning. Differences between the TPS and the FDC in SFO and MFO IMPT plans depend strongly on beam arrangement and the presence of a heterogeneous body. We advocate use of a Monte Carlo method in proton treatment planning to deliver the most precise proton dose in IMPT.
基金This research is supported by the National Natural Science Foundation of China(62076185,U1809209)Zhejiang Provincial Natural Science Foundation of China(LY21F020030)+2 种基金Wenzhou Science&Technology Bureau(2018ZG016)Taif University Researchers Supporting Project Number(TURSP-2020/125)Taif University,Taif,Saudi Arabia。
文摘In the original Moth-Flame Optimization(MFO),the search behavior of the moth depends on the corresponding flame and the interaction between the moth and its corresponding flame,so it will get stuck in the local optimum easily when facing the multi-dimensional and high-dimensional optimization problems.Therefore,in this work,a generalized oppositional MFO with crossover strategy,named GCMFO,is presented to overcome the mentioned defects.In the proposed GCMFO,GOBL is employed to increase the population diversity and expand the search range in the initialization and iteration jump phase based on the jump rate;crisscross search(CC)is adopted to promote the exploitation and/or exploration ability of MFO.The proposed algorithm’s performance is estimated by organizing a series of experiments;firstly,the CEC2017 benchmark set is adopted to evaluate the performance of GCMFO in tackling high-dimensional and multimodal problems.Secondly,GCMFO is applied to handle multilevel thresholding image segmentation problems.At last,GCMFO is integrated into kernel extreme learning machine classifier to deal with three medical diagnosis cases,including the appendicitis diagnosis,overweight statuses diagnosis,and thyroid cancer diagnosis.Experimental results and discussions show that the proposed approach outperforms the original MFO and other state-of-the-art algorithms on both convergence speed and accuracy.It also indicates that the presented GCMFO has a promising potential for application.
文摘The software engineering technique makes it possible to create high-quality software.One of the most significant qualities of good software is that it is devoid of bugs.One of the most time-consuming and costly software proce-dures isfinding andfixing bugs.Although it is impossible to eradicate all bugs,it is feasible to reduce the number of bugs and their negative effects.To broaden the scope of bug prediction techniques and increase software quality,numerous causes of software problems must be identified,and successful bug prediction models must be implemented.This study employs a hybrid of Faster Convolution Neural Network and the Moth Flame Optimization(MFO)algorithm to forecast the number of bugs in software based on the program data itself,such as the line quantity in codes,methods characteristics,and other essential software aspects.Here,the MFO method is used to train the neural network to identify optimal weights.The proposed MFO-FCNN technique is compared with existing methods such as AdaBoost(AB),Random Forest(RF),K-Nearest Neighbour(KNN),K-Means Clustering(KMC),Support Vector Machine(SVM)and Bagging Clas-sifier(BC)are examples of machine learning(ML)techniques.The assessment method revealed that machine learning techniques may be employed successfully and through a high level of accuracy.The obtained data revealed that the proposed strategy outperforms the traditional approach.
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。
