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
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
This paper proposes a modified version of the Dwarf Mongoose Optimization Algorithm (IDMO) for constrained engineering design problems. This optimization technique modifies the base algorithm (DMO) in three simple but...This paper proposes a modified version of the Dwarf Mongoose Optimization Algorithm (IDMO) for constrained engineering design problems. This optimization technique modifies the base algorithm (DMO) in three simple but effective ways. First, the alpha selection in IDMO differs from the DMO, where evaluating the probability value of each fitness is just a computational overhead and contributes nothing to the quality of the alpha or other group members. The fittest dwarf mongoose is selected as the alpha, and a new operator ω is introduced, which controls the alpha movement, thereby enhancing the exploration ability and exploitability of the IDMO. Second, the scout group movements are modified by randomization to introduce diversity in the search process and explore unvisited areas. Finally, the babysitter's exchange criterium is modified such that once the criterium is met, the babysitters that are exchanged interact with the dwarf mongoose exchanging them to gain information about food sources and sleeping mounds, which could result in better-fitted mongooses instead of initializing them afresh as done in DMO, then the counter is reset to zero. The proposed IDMO was used to solve the classical and CEC 2020 benchmark functions and 12 continuous/discrete engineering optimization problems. The performance of the IDMO, using different performance metrics and statistical analysis, is compared with the DMO and eight other existing algorithms. In most cases, the results show that solutions achieved by the IDMO are better than those obtained by the existing algorithms.展开更多
The Egyptia n mon goose (Herpestes ichneumon Linn aeus, 1758) is a medium-sized car nivore that experienced remarkable geographic expansion over the last 3 decades in the Iberian Peninsula. In this study, we investiga...The Egyptia n mon goose (Herpestes ichneumon Linn aeus, 1758) is a medium-sized car nivore that experienced remarkable geographic expansion over the last 3 decades in the Iberian Peninsula. In this study, we investigated the association of species-related and abiotic factors with spleen weight (as a proxy for immunocompete nee) in the species. We assessed the relationship of body con dition, sex, age, seas on, and envir onmental conditi ons with splee n weight established for 508 hunted specimens. Our results indicate that the effects of sex and season outweigh those of all other variables, including body condition. Spleen weight is higher in males than in females, and heavier spleens are more likely to be found in spring, coinciding with the highest period of investment in reproduction due to mating, gestation, birth, and lactation. Coupled with the absence of an effect of body condition, our findi ngs suggest that splee n weight variation in this species is mostly influe need by lifehistory traits linked to reproduction, rather than overall energy availability, winter immunoenhancement, or energy partitioning effects, and prompt further research focusing on this topic.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
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
Objective:To investigate the infection with gastrointestinal helminthes in small Indian mongooses(Herpestes auropunctatus)and its epidemiologic aspects in Iran.Methods:During June 2012 to July 2013,a total of 13 small...Objective:To investigate the infection with gastrointestinal helminthes in small Indian mongooses(Herpestes auropunctatus)and its epidemiologic aspects in Iran.Methods:During June 2012 to July 2013,a total of 13 small Indian mongooses were caught using live trap boxes in an area located near Shiraz,southern of Iran.Captured animals were euthanized,eviscerated and parts of the alimentary tract were inspected.Two mongooses showed a nematode attached to the mucosa of the stomach.Results:According to the main morphological characteristics,the specimens belonged to the genus Spirura(Blanchard 1849).This study represents the first evidences of the infection withSpirura sp.in Herpestes auropunctatus in the world.Conclusions:Because the animal can invade and appear in the habitat of the other animal populations including omnivores or carnivores,it seems that mongooses in this area could have a high potential for the transmission of the infection with the spirurid nematodes to a large range of animals.Thus,besides the necessity of conducting the controlling programs,autochthonous dogs,cats and rodents should be included in more epidemiological studies in this region.展开更多
It has been suggested that spatial heterogeneity is key to the coexistence at local spatial scales of subordinate and dominant predator species by allowing the former to shift to more protective habitats when the risk...It has been suggested that spatial heterogeneity is key to the coexistence at local spatial scales of subordinate and dominant predator species by allowing the former to shift to more protective habitats when the risk of intraguild predation exists. Here, we show how the smaller carnivore Egyptian mongoose (Herpestes ichneumon) may coexist on a local scale with its intraguild pre- dator, the Iberian lynx (Lynx pardinus), by using places with different microhabitat character- istics. We expect that mongooses living within lynx home ranges will use denser and more protective habitats when active in order to di- minish their risk of being killed by lynx com- pared to those living in areas similar in vege- tation and prey availability but where lynx are absent. The scrubland cover of points used by mongooses outside lynx areas, and that of points located within lynx areas but not used by mongooses, were significantly lower than, or similar to, cover of points used by mongooses within lynx areas. The probability of finding mon- goose tracks was constant across levels of scrubland cover when lynx were absent, but more mongoose tracks were likely to be found in thicker scrubland within lynx areas, especially if these areas were intensively used by lynx. This result agrees with the hypothesis on shifts in microhabitat use of subordinate carnivores to prevent fatal or risky encounters with dominant ones.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.
文摘This paper proposes a modified version of the Dwarf Mongoose Optimization Algorithm (IDMO) for constrained engineering design problems. This optimization technique modifies the base algorithm (DMO) in three simple but effective ways. First, the alpha selection in IDMO differs from the DMO, where evaluating the probability value of each fitness is just a computational overhead and contributes nothing to the quality of the alpha or other group members. The fittest dwarf mongoose is selected as the alpha, and a new operator ω is introduced, which controls the alpha movement, thereby enhancing the exploration ability and exploitability of the IDMO. Second, the scout group movements are modified by randomization to introduce diversity in the search process and explore unvisited areas. Finally, the babysitter's exchange criterium is modified such that once the criterium is met, the babysitters that are exchanged interact with the dwarf mongoose exchanging them to gain information about food sources and sleeping mounds, which could result in better-fitted mongooses instead of initializing them afresh as done in DMO, then the counter is reset to zero. The proposed IDMO was used to solve the classical and CEC 2020 benchmark functions and 12 continuous/discrete engineering optimization problems. The performance of the IDMO, using different performance metrics and statistical analysis, is compared with the DMO and eight other existing algorithms. In most cases, the results show that solutions achieved by the IDMO are better than those obtained by the existing algorithms.
文摘The Egyptia n mon goose (Herpestes ichneumon Linn aeus, 1758) is a medium-sized car nivore that experienced remarkable geographic expansion over the last 3 decades in the Iberian Peninsula. In this study, we investigated the association of species-related and abiotic factors with spleen weight (as a proxy for immunocompete nee) in the species. We assessed the relationship of body con dition, sex, age, seas on, and envir onmental conditi ons with splee n weight established for 508 hunted specimens. Our results indicate that the effects of sex and season outweigh those of all other variables, including body condition. Spleen weight is higher in males than in females, and heavier spleens are more likely to be found in spring, coinciding with the highest period of investment in reproduction due to mating, gestation, birth, and lactation. Coupled with the absence of an effect of body condition, our findi ngs suggest that splee n weight variation in this species is mostly influe need by lifehistory traits linked to reproduction, rather than overall energy availability, winter immunoenhancement, or energy partitioning effects, and prompt further research focusing on this topic.
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Supported by the financial support from Shiraz University,Shiraz,Iran(Grant No.VE-1218-1316-70).
文摘Objective:To investigate the infection with gastrointestinal helminthes in small Indian mongooses(Herpestes auropunctatus)and its epidemiologic aspects in Iran.Methods:During June 2012 to July 2013,a total of 13 small Indian mongooses were caught using live trap boxes in an area located near Shiraz,southern of Iran.Captured animals were euthanized,eviscerated and parts of the alimentary tract were inspected.Two mongooses showed a nematode attached to the mucosa of the stomach.Results:According to the main morphological characteristics,the specimens belonged to the genus Spirura(Blanchard 1849).This study represents the first evidences of the infection withSpirura sp.in Herpestes auropunctatus in the world.Conclusions:Because the animal can invade and appear in the habitat of the other animal populations including omnivores or carnivores,it seems that mongooses in this area could have a high potential for the transmission of the infection with the spirurid nematodes to a large range of animals.Thus,besides the necessity of conducting the controlling programs,autochthonous dogs,cats and rodents should be included in more epidemiological studies in this region.
基金funded by project CGL2004-00346/BOS of Ministry of Education and Sciencesupported by a predoctoral grant of CSIC-Spanish Council for Research,“I3P”programsupported by a FPU and a post-doctoral fellowships from the Spanish Ministry of Education.
文摘It has been suggested that spatial heterogeneity is key to the coexistence at local spatial scales of subordinate and dominant predator species by allowing the former to shift to more protective habitats when the risk of intraguild predation exists. Here, we show how the smaller carnivore Egyptian mongoose (Herpestes ichneumon) may coexist on a local scale with its intraguild pre- dator, the Iberian lynx (Lynx pardinus), by using places with different microhabitat character- istics. We expect that mongooses living within lynx home ranges will use denser and more protective habitats when active in order to di- minish their risk of being killed by lynx com- pared to those living in areas similar in vege- tation and prey availability but where lynx are absent. The scrubland cover of points used by mongooses outside lynx areas, and that of points located within lynx areas but not used by mongooses, were significantly lower than, or similar to, cover of points used by mongooses within lynx areas. The probability of finding mon- goose tracks was constant across levels of scrubland cover when lynx were absent, but more mongoose tracks were likely to be found in thicker scrubland within lynx areas, especially if these areas were intensively used by lynx. This result agrees with the hypothesis on shifts in microhabitat use of subordinate carnivores to prevent fatal or risky encounters with dominant ones.
