Wireless sensor network deployment optimization is a classic NP-hard problem and a popular topic in academic research.However,the current research on wireless sensor network deployment problems uses overly simplistic ...Wireless sensor network deployment optimization is a classic NP-hard problem and a popular topic in academic research.However,the current research on wireless sensor network deployment problems uses overly simplistic models,and there is a significant gap between the research results and actual wireless sensor networks.Some scholars have now modeled data fusion networks to make them more suitable for practical applications.This paper will explore the deployment problem of a stochastic data fusion wireless sensor network(SDFWSN),a model that reflects the randomness of environmental monitoring and uses data fusion techniques widely used in actual sensor networks for information collection.The deployment problem of SDFWSN is modeled as a multi-objective optimization problem.The network life cycle,spatiotemporal coverage,detection rate,and false alarm rate of SDFWSN are used as optimization objectives to optimize the deployment of network nodes.This paper proposes an enhanced multi-objective mongoose optimization algorithm(EMODMOA)to solve the deployment problem of SDFWSN.First,to overcome the shortcomings of the DMOA algorithm,such as its low convergence and tendency to get stuck in a local optimum,an encircling and hunting strategy is introduced into the original algorithm to propose the EDMOA algorithm.The EDMOA algorithm is designed as the EMODMOA algorithm by selecting reference points using the K-Nearest Neighbor(KNN)algorithm.To verify the effectiveness of the proposed algorithm,the EMODMOA algorithm was tested at CEC 2020 and achieved good results.In the SDFWSN deployment problem,the algorithm was compared with the Non-dominated Sorting Genetic Algorithm II(NSGAII),Multiple Objective Particle Swarm Optimization(MOPSO),Multi-Objective Evolutionary Algorithm based on Decomposition(MOEA/D),and Multi-Objective Grey Wolf Optimizer(MOGWO).By comparing and analyzing the performance evaluation metrics and optimization results of the objective functions of the multi-objective algorithms,the algorithm outperforms the other algorithms in the SDFWSN deployment results.To better demonstrate the superiority of the algorithm,simulations of diverse test cases were also performed,and good results were obtained.展开更多
Feature selection(FS)plays a crucial role in pre-processing machine learning datasets,as it eliminates redundant features to improve classification accuracy and reduce computational costs.This paper presents an enhanc...Feature selection(FS)plays a crucial role in pre-processing machine learning datasets,as it eliminates redundant features to improve classification accuracy and reduce computational costs.This paper presents an enhanced approach to FS for software fault prediction,specifically by enhancing the binary dwarf mongoose optimization(BDMO)algorithm with a crossover mechanism and a modified positioning updating formula.The proposed approach,termed iBDMOcr,aims to fortify exploration capability,promote population diversity,and lastly improve the wrapper-based FS process for software fault prediction tasks.iBDMOcr gained superb performance compared to other well-esteemed optimization methods across 17 benchmark datasets.It ranked first in 11 out of 17 datasets in terms of average classification accuracy.Moreover,iBDMOcr outperformed other methods in terms of average fitness values and number of selected features across all datasets.The findings demonstrate the effectiveness of iBDMOcr in addressing FS problems in software fault prediction,leading to more accurate and efficient models.展开更多
Cognitive radio wireless sensor networks(CRWSN)can be defined as a promising technology for developing bandwidth-limited applications.CRWSN is widely utilized by future Internet of Things(IoT)applications.Since a prom...Cognitive radio wireless sensor networks(CRWSN)can be defined as a promising technology for developing bandwidth-limited applications.CRWSN is widely utilized by future Internet of Things(IoT)applications.Since a promising technology,Cognitive Radio(CR)can be modelled to alleviate the spectrum scarcity issue.Generally,CRWSN has cognitive radioenabled sensor nodes(SNs),which are energy limited.Hierarchical clusterrelated techniques for overall network management can be suitable for the scalability and stability of the network.This paper focuses on designing the Modified Dwarf Mongoose Optimization Enabled Energy Aware Clustering(MDMO-EAC)Scheme for CRWSN.The MDMO-EAC technique mainly intends to group the nodes into clusters in the CRWSN.Besides,theMDMOEAC algorithm is based on the dwarf mongoose optimization(DMO)algorithm design with oppositional-based learning(OBL)concept for the clustering process,showing the novelty of the work.In addition,the presented MDMO-EAC algorithm computed a multi-objective function for improved network efficiency.The presented model is validated using a comprehensive range of experiments,and the outcomes were scrutinized in varying measures.The comparison study stated the improvements of the MDMO-EAC method over other recent approaches.展开更多
In response to the shortcomings of Dwarf Mongoose Optimization(DMO)algorithm,such as insufficient exploitation capability and slow convergence speed,this paper proposes a multi-strategy enhanced DMO,referred to as GLS...In response to the shortcomings of Dwarf Mongoose Optimization(DMO)algorithm,such as insufficient exploitation capability and slow convergence speed,this paper proposes a multi-strategy enhanced DMO,referred to as GLSDMO.Firstly,we propose an improved solution search equation that utilizes the Gbest-guided strategy with different parameters to achieve a trade-off between exploration and exploitation(EE).Secondly,the Lévy flight is introduced to increase the diversity of population distribution and avoid the algorithm getting stuck in a local optimum.In addition,in order to address the problem of low convergence efficiency of DMO,this study uses the strong nonlinear convergence factor Sigmaid function as the moving step size parameter of the mongoose during collective activities,and combines the strategy of the salp swarm leader with the mongoose for cooperative optimization,which enhances the search efficiency of agents and accelerating the convergence of the algorithm to the global optimal solution(Gbest).Subsequently,the superiority of GLSDMO is verified on CEC2017 and CEC2019,and the optimization effect of GLSDMO is analyzed in detail.The results show that GLSDMO is significantly superior to the compared algorithms in solution quality,robustness and global convergence rate on most test functions.Finally,the optimization performance of GLSDMO is verified on three classic engineering examples and one truss topology optimization example.The simulation results show that GLSDMO achieves optimal costs on these real-world engineering problems.展开更多
基金supported by the National Natural Science Foundation of China under Grant Nos.U21A20464,62066005Innovation Project of Guangxi Graduate Education under Grant No.YCSW2024313.
文摘Wireless sensor network deployment optimization is a classic NP-hard problem and a popular topic in academic research.However,the current research on wireless sensor network deployment problems uses overly simplistic models,and there is a significant gap between the research results and actual wireless sensor networks.Some scholars have now modeled data fusion networks to make them more suitable for practical applications.This paper will explore the deployment problem of a stochastic data fusion wireless sensor network(SDFWSN),a model that reflects the randomness of environmental monitoring and uses data fusion techniques widely used in actual sensor networks for information collection.The deployment problem of SDFWSN is modeled as a multi-objective optimization problem.The network life cycle,spatiotemporal coverage,detection rate,and false alarm rate of SDFWSN are used as optimization objectives to optimize the deployment of network nodes.This paper proposes an enhanced multi-objective mongoose optimization algorithm(EMODMOA)to solve the deployment problem of SDFWSN.First,to overcome the shortcomings of the DMOA algorithm,such as its low convergence and tendency to get stuck in a local optimum,an encircling and hunting strategy is introduced into the original algorithm to propose the EDMOA algorithm.The EDMOA algorithm is designed as the EMODMOA algorithm by selecting reference points using the K-Nearest Neighbor(KNN)algorithm.To verify the effectiveness of the proposed algorithm,the EMODMOA algorithm was tested at CEC 2020 and achieved good results.In the SDFWSN deployment problem,the algorithm was compared with the Non-dominated Sorting Genetic Algorithm II(NSGAII),Multiple Objective Particle Swarm Optimization(MOPSO),Multi-Objective Evolutionary Algorithm based on Decomposition(MOEA/D),and Multi-Objective Grey Wolf Optimizer(MOGWO).By comparing and analyzing the performance evaluation metrics and optimization results of the objective functions of the multi-objective algorithms,the algorithm outperforms the other algorithms in the SDFWSN deployment results.To better demonstrate the superiority of the algorithm,simulations of diverse test cases were also performed,and good results were obtained.
基金supported by the Deanship of Scientific Research and Innovation at Al-Balqa Applied University in Jordan.
文摘Feature selection(FS)plays a crucial role in pre-processing machine learning datasets,as it eliminates redundant features to improve classification accuracy and reduce computational costs.This paper presents an enhanced approach to FS for software fault prediction,specifically by enhancing the binary dwarf mongoose optimization(BDMO)algorithm with a crossover mechanism and a modified positioning updating formula.The proposed approach,termed iBDMOcr,aims to fortify exploration capability,promote population diversity,and lastly improve the wrapper-based FS process for software fault prediction tasks.iBDMOcr gained superb performance compared to other well-esteemed optimization methods across 17 benchmark datasets.It ranked first in 11 out of 17 datasets in terms of average classification accuracy.Moreover,iBDMOcr outperformed other methods in terms of average fitness values and number of selected features across all datasets.The findings demonstrate the effectiveness of iBDMOcr in addressing FS problems in software fault prediction,leading to more accurate and efficient models.
基金This research work was funded by Institutional Fund Projects under grant no.(IFPIP:14-611-1443)Therefore,the authors gratefully acknowledge technical and financial support provided by the Ministry of Education and Deanship of Scientific Research(DSR),King Abdulaziz University(KAU),Jeddah,Saudi Arabia.
文摘Cognitive radio wireless sensor networks(CRWSN)can be defined as a promising technology for developing bandwidth-limited applications.CRWSN is widely utilized by future Internet of Things(IoT)applications.Since a promising technology,Cognitive Radio(CR)can be modelled to alleviate the spectrum scarcity issue.Generally,CRWSN has cognitive radioenabled sensor nodes(SNs),which are energy limited.Hierarchical clusterrelated techniques for overall network management can be suitable for the scalability and stability of the network.This paper focuses on designing the Modified Dwarf Mongoose Optimization Enabled Energy Aware Clustering(MDMO-EAC)Scheme for CRWSN.The MDMO-EAC technique mainly intends to group the nodes into clusters in the CRWSN.Besides,theMDMOEAC algorithm is based on the dwarf mongoose optimization(DMO)algorithm design with oppositional-based learning(OBL)concept for the clustering process,showing the novelty of the work.In addition,the presented MDMO-EAC algorithm computed a multi-objective function for improved network efficiency.The presented model is validated using a comprehensive range of experiments,and the outcomes were scrutinized in varying measures.The comparison study stated the improvements of the MDMO-EAC method over other recent approaches.
基金National Natural Science Foundation of China,Grant No.52375264.
文摘In response to the shortcomings of Dwarf Mongoose Optimization(DMO)algorithm,such as insufficient exploitation capability and slow convergence speed,this paper proposes a multi-strategy enhanced DMO,referred to as GLSDMO.Firstly,we propose an improved solution search equation that utilizes the Gbest-guided strategy with different parameters to achieve a trade-off between exploration and exploitation(EE).Secondly,the Lévy flight is introduced to increase the diversity of population distribution and avoid the algorithm getting stuck in a local optimum.In addition,in order to address the problem of low convergence efficiency of DMO,this study uses the strong nonlinear convergence factor Sigmaid function as the moving step size parameter of the mongoose during collective activities,and combines the strategy of the salp swarm leader with the mongoose for cooperative optimization,which enhances the search efficiency of agents and accelerating the convergence of the algorithm to the global optimal solution(Gbest).Subsequently,the superiority of GLSDMO is verified on CEC2017 and CEC2019,and the optimization effect of GLSDMO is analyzed in detail.The results show that GLSDMO is significantly superior to the compared algorithms in solution quality,robustness and global convergence rate on most test functions.Finally,the optimization performance of GLSDMO is verified on three classic engineering examples and one truss topology optimization example.The simulation results show that GLSDMO achieves optimal costs on these real-world engineering problems.
