Wireless Sensor Networks(WSNs)are a collection of sensor nodes distributed in space and connected through wireless communication.The sensor nodes gather and store data about the real world around them.However,the node...Wireless Sensor Networks(WSNs)are a collection of sensor nodes distributed in space and connected through wireless communication.The sensor nodes gather and store data about the real world around them.However,the nodes that are dependent on batteries will ultimately suffer an energy loss with time,which affects the lifetime of the network.This research proposes to achieve its primary goal by reducing energy consumption and increasing the network’s lifetime and stability.The present technique employs the hybrid Mayfly Optimization Algorithm-Enhanced Ant Colony Optimization(MFOA-EACO),where the Mayfly Optimization Algorithm(MFOA)is used to select the best cluster head(CH)from a set of nodes,and the Enhanced Ant Colony Optimization(EACO)technique is used to determine an optimal route between the cluster head and base station.The performance evaluation of our suggested hybrid approach is based on many parameters,including the number of active and dead nodes,node degree,distance,and energy usage.Our objective is to integrate MFOA-EACO to enhance energy efficiency and extend the network life of the WSN in the future.The proposed method outcomes proved to be better than traditional approaches such as Hybrid Squirrel-Flying Fox Optimization Algorithm(HSFLBOA),Hybrid Social Reindeer Optimization and Differential Evolution-Firefly Algorithm(HSRODE-FFA),Social Spider Distance Sensitive-Iterative Antlion Butterfly Cockroach Algorithm(SADSS-IABCA),and Energy Efficient Clustering Hierarchy Strategy-Improved Social Spider Algorithm Differential Evolution(EECHS-ISSADE).展开更多
Energy conservation is becoming the main critical issue in wireless sensor network and also the main research area for most of the researchers. For improving the energy efficiency, sink mobility is used with constrain...Energy conservation is becoming the main critical issue in wireless sensor network and also the main research area for most of the researchers. For improving the energy efficiency, sink mobility is used with constrain path in wireless sensor network. In order to solve these optimization problems, inter cluster Ant Colony Optimization Algorithm (ACO) is used with mobile sink (MS) and rendezvous nodes (RN). The proposed algorithm will improve 30% more network lifetime than the existing algorithm and prompts high accurate delivery of packets in highly dense network.展开更多
To solve the traveling salesman problem with the characteristics of clustering,a novel hybrid algorithm,the ant colony algorithm combined with the C-means algorithm,is presented.In order to improve the speed of conver...To solve the traveling salesman problem with the characteristics of clustering,a novel hybrid algorithm,the ant colony algorithm combined with the C-means algorithm,is presented.In order to improve the speed of convergence,the traveling salesman problem(TSP)data is specially clustered by the C-means algorithm,then,the result is processed by the ant colony algorithm to solve the problem.The proposed algorithm treats the C-means algorithm as a new search operator and adopts a kind of local searching strategy—2-opt,so as to improve the searching performance.Given the cluster number,the algorithm can obtain the preferable solving result.Compared with the three other algorithms—the ant colony algorithm,the genetic algorithm and the simulated annealing algorithm,the proposed algorithm can make the results converge to the global optimum faster and it has higher accuracy.The algorithm can also be extended to solve other correlative clustering combination optimization problems.Experimental results indicate the validity of the proposed algorithm.展开更多
This paper proposes a novel energy efficient unequal clustering algorithm for large scale wireless sensor network （WSN） which aims to balance the node power consumption and prolong the network lifetime as long as po...This paper proposes a novel energy efficient unequal clustering algorithm for large scale wireless sensor network （WSN） which aims to balance the node power consumption and prolong the network lifetime as long as possible. Our approach focuses on energy efficient unequal clustering scheme and inter-cluster routing protocol. On the one hand, considering each node＇s local information such as energy level, distance to base station and local density, we use fuzzy logic system to determine one node＇s chance of becoming cluster head and hand, adaptive max-min ant colony optimization is used to estimate the corresponding competence radius. On the other construct energy-aware inter-cluster routing between cluster heads and base station （BS）, which balances the energy consumption of cluster heads and alleviates the hot spots problem that occurs in multi-hop WSN routing protocol to a large extent. The confirmation experiment results have indicated the proposed clustering algorithm has more superior performance than other methods such as low energy adaptive clustering hierarchy （LEACH） and energy efficient unequal clustering （EEUC）.展开更多
The current mathematical models for the storage assignment problem are generally established based on the traveling salesman problem（TSP）,which has been widely applied in the conventional automated storage and retri...The current mathematical models for the storage assignment problem are generally established based on the traveling salesman problem（TSP）,which has been widely applied in the conventional automated storage and retrieval system（AS/RS）.However,the previous mathematical models in conventional AS/RS do not match multi-tier shuttle warehousing systems（MSWS） because the characteristics of parallel retrieval in multiple tiers and progressive vertical movement destroy the foundation of TSP.In this study,a two-stage open queuing network model in which shuttles and a lift are regarded as servers at different stages is proposed to analyze system performance in the terms of shuttle waiting period（SWP） and lift idle period（LIP） during transaction cycle time.A mean arrival time difference matrix for pairwise stock keeping units（SKUs） is presented to determine the mean waiting time and queue length to optimize the storage assignment problem on the basis of SKU correlation.The decomposition method is applied to analyze the interactions among outbound task time,SWP,and LIP.The ant colony clustering algorithm is designed to determine storage partitions using clustering items.In addition,goods are assigned for storage according to the rearranging permutation and the combination of storage partitions in a 2D plane.This combination is derived based on the analysis results of the queuing network model and on three basic principles.The storage assignment method and its entire optimization algorithm method as applied in a MSWS are verified through a practical engineering project conducted in the tobacco industry.The applying results show that the total SWP and LIP can be reduced effectively to improve the utilization rates of all devices and to increase the throughput of the distribution center.展开更多
introduce a new kind of swarm intelligence algorithm, the Ant Colony Optimization （ACO） algorithm. Propose a clustering analysis model based on ACO, apply the model to recognition and diagnosis of operation state fo...introduce a new kind of swarm intelligence algorithm, the Ant Colony Optimization （ACO） algorithm. Propose a clustering analysis model based on ACO, apply the model to recognition and diagnosis of operation state for gearbox. Testing four kinds of gears and clustering some characteristic parameters of the gear vibration signal, the conclusion shows that this method can recognize running state with accuracy and all speed. It is a new method for fault recognition and diagnosis.展开更多
文摘Wireless Sensor Networks(WSNs)are a collection of sensor nodes distributed in space and connected through wireless communication.The sensor nodes gather and store data about the real world around them.However,the nodes that are dependent on batteries will ultimately suffer an energy loss with time,which affects the lifetime of the network.This research proposes to achieve its primary goal by reducing energy consumption and increasing the network’s lifetime and stability.The present technique employs the hybrid Mayfly Optimization Algorithm-Enhanced Ant Colony Optimization(MFOA-EACO),where the Mayfly Optimization Algorithm(MFOA)is used to select the best cluster head(CH)from a set of nodes,and the Enhanced Ant Colony Optimization(EACO)technique is used to determine an optimal route between the cluster head and base station.The performance evaluation of our suggested hybrid approach is based on many parameters,including the number of active and dead nodes,node degree,distance,and energy usage.Our objective is to integrate MFOA-EACO to enhance energy efficiency and extend the network life of the WSN in the future.The proposed method outcomes proved to be better than traditional approaches such as Hybrid Squirrel-Flying Fox Optimization Algorithm(HSFLBOA),Hybrid Social Reindeer Optimization and Differential Evolution-Firefly Algorithm(HSRODE-FFA),Social Spider Distance Sensitive-Iterative Antlion Butterfly Cockroach Algorithm(SADSS-IABCA),and Energy Efficient Clustering Hierarchy Strategy-Improved Social Spider Algorithm Differential Evolution(EECHS-ISSADE).
文摘Energy conservation is becoming the main critical issue in wireless sensor network and also the main research area for most of the researchers. For improving the energy efficiency, sink mobility is used with constrain path in wireless sensor network. In order to solve these optimization problems, inter cluster Ant Colony Optimization Algorithm (ACO) is used with mobile sink (MS) and rendezvous nodes (RN). The proposed algorithm will improve 30% more network lifetime than the existing algorithm and prompts high accurate delivery of packets in highly dense network.
基金The National Key Technology R&D Program of China during the 11th Five-Year Plan Period(No.2006BAH02A06)
文摘To solve the traveling salesman problem with the characteristics of clustering,a novel hybrid algorithm,the ant colony algorithm combined with the C-means algorithm,is presented.In order to improve the speed of convergence,the traveling salesman problem(TSP)data is specially clustered by the C-means algorithm,then,the result is processed by the ant colony algorithm to solve the problem.The proposed algorithm treats the C-means algorithm as a new search operator and adopts a kind of local searching strategy—2-opt,so as to improve the searching performance.Given the cluster number,the algorithm can obtain the preferable solving result.Compared with the three other algorithms—the ant colony algorithm,the genetic algorithm and the simulated annealing algorithm,the proposed algorithm can make the results converge to the global optimum faster and it has higher accuracy.The algorithm can also be extended to solve other correlative clustering combination optimization problems.Experimental results indicate the validity of the proposed algorithm.
基金supported by National Science and Technology Major Project of the Ministry of Science and Technology of China (2009ZX03006-006, 2009ZX03006-009)the National Natural Science Foundation of China (60902046, 60972079)
文摘This paper proposes a novel energy efficient unequal clustering algorithm for large scale wireless sensor network （WSN） which aims to balance the node power consumption and prolong the network lifetime as long as possible. Our approach focuses on energy efficient unequal clustering scheme and inter-cluster routing protocol. On the one hand, considering each node＇s local information such as energy level, distance to base station and local density, we use fuzzy logic system to determine one node＇s chance of becoming cluster head and hand, adaptive max-min ant colony optimization is used to estimate the corresponding competence radius. On the other construct energy-aware inter-cluster routing between cluster heads and base station （BS）, which balances the energy consumption of cluster heads and alleviates the hot spots problem that occurs in multi-hop WSN routing protocol to a large extent. The confirmation experiment results have indicated the proposed clustering algorithm has more superior performance than other methods such as low energy adaptive clustering hierarchy （LEACH） and energy efficient unequal clustering （EEUC）.
基金Supported by National Natural Science Foundation of China(Grant No.661403234)Shandong Provincial Science and Techhnology Development Plan of China(Grant No.2014GGX106009)
文摘The current mathematical models for the storage assignment problem are generally established based on the traveling salesman problem（TSP）,which has been widely applied in the conventional automated storage and retrieval system（AS/RS）.However,the previous mathematical models in conventional AS/RS do not match multi-tier shuttle warehousing systems（MSWS） because the characteristics of parallel retrieval in multiple tiers and progressive vertical movement destroy the foundation of TSP.In this study,a two-stage open queuing network model in which shuttles and a lift are regarded as servers at different stages is proposed to analyze system performance in the terms of shuttle waiting period（SWP） and lift idle period（LIP） during transaction cycle time.A mean arrival time difference matrix for pairwise stock keeping units（SKUs） is presented to determine the mean waiting time and queue length to optimize the storage assignment problem on the basis of SKU correlation.The decomposition method is applied to analyze the interactions among outbound task time,SWP,and LIP.The ant colony clustering algorithm is designed to determine storage partitions using clustering items.In addition,goods are assigned for storage according to the rearranging permutation and the combination of storage partitions in a 2D plane.This combination is derived based on the analysis results of the queuing network model and on three basic principles.The storage assignment method and its entire optimization algorithm method as applied in a MSWS are verified through a practical engineering project conducted in the tobacco industry.The applying results show that the total SWP and LIP can be reduced effectively to improve the utilization rates of all devices and to increase the throughput of the distribution center.
文摘introduce a new kind of swarm intelligence algorithm, the Ant Colony Optimization （ACO） algorithm. Propose a clustering analysis model based on ACO, apply the model to recognition and diagnosis of operation state for gearbox. Testing four kinds of gears and clustering some characteristic parameters of the gear vibration signal, the conclusion shows that this method can recognize running state with accuracy and all speed. It is a new method for fault recognition and diagnosis.
