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Safety Evaluation Method of Evacuation Routes in Areas in Case of Earthquake Disasters Using Ant Optimization Algorithm and Geographic Information Systems
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作者 Kayoko Yamamoto Ximing Li 《Journal of Environmental Science and Engineering(A)》 2017年第9期462-478,共17页
The present study aims to propose the method for the quantitative evaluation of safety concerning evacuation routes in case of earthquake disasters in urban areas using ACO (Ant Colony Optimization) algorithm and G... The present study aims to propose the method for the quantitative evaluation of safety concerning evacuation routes in case of earthquake disasters in urban areas using ACO (Ant Colony Optimization) algorithm and GIS (Geographic Information Systems). Regarding the safety evaluation method, firstly, the similarity in safety was focused on while taking into consideration road blockage probability, and after classifying roads by means of the hierarchical cluster analysis, the congestion rates of evacuation routes using ACO simulations were estimated. Based on these results, the multiple evacuation routes extracted were visualized on digital maps by means of GIS, and its safety was evaluated. Furthermore, the selection of safe evacuation routes between evacuation sites, for cases when the possibility of large-scale evacuation after an earthquake disaster is high, is made possible. As the safety evaluation method is based on public information, by obtaining the same geographic information as the present study, it is effective in other areas regardless of whether the information is of the past and future. Therefore, in addition to spatial reproducibility, the safety evaluation method also has high temporal reproducibility. Because safety evaluations are conducted on evacuation routes based on quantified data, highly safe evacuation routes that are selected have been quantitatively evaluated, and thus serve as an effective indicator when selecting evacuation routes. 展开更多
关键词 Large-scale evacuation evacuation route safety evaluation earthquake disaster ACO ant Colony optimization GIS (Geographic Information Systems).
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Multi-Label Feature Selection Based on Improved Ant Colony Optimization Algorithm with Dynamic Redundancy and Label Dependence
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作者 Ting Cai Chun Ye +5 位作者 Zhiwei Ye Ziyuan Chen Mengqing Mei Haichao Zhang Wanfang Bai Peng Zhang 《Computers, Materials & Continua》 SCIE EI 2024年第10期1157-1175,共19页
The world produces vast quantities of high-dimensional multi-semantic data.However,extracting valuable information from such a large amount of high-dimensional and multi-label data is undoubtedly arduous and challengi... The world produces vast quantities of high-dimensional multi-semantic data.However,extracting valuable information from such a large amount of high-dimensional and multi-label data is undoubtedly arduous and challenging.Feature selection aims to mitigate the adverse impacts of high dimensionality in multi-label data by eliminating redundant and irrelevant features.The ant colony optimization algorithm has demonstrated encouraging outcomes in multi-label feature selection,because of its simplicity,efficiency,and similarity to reinforcement learning.Nevertheless,existing methods do not consider crucial correlation information,such as dynamic redundancy and label correlation.To tackle these concerns,the paper proposes a multi-label feature selection technique based on ant colony optimization algorithm(MFACO),focusing on dynamic redundancy and label correlation.Initially,the dynamic redundancy is assessed between the selected feature subset and potential features.Meanwhile,the ant colony optimization algorithm extracts label correlation from the label set,which is then combined into the heuristic factor as label weights.Experimental results demonstrate that our proposed strategies can effectively enhance the optimal search ability of ant colony,outperforming the other algorithms involved in the paper. 展开更多
关键词 Multi-label feature selection ant colony optimization algorithm dynamic redundancy high-dimensional data label correlation
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Bio-Inspired Intelligent Routing in WSN: Integrating Mayfly Optimization and Enhanced Ant Colony Optimization for Energy-Efficient Cluster Formation and Maintenance
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作者 V.G.Saranya S.Karthik 《Computer Modeling in Engineering & Sciences》 SCIE EI 2024年第10期127-150,共24页
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
关键词 Enhanced ant colony optimization mayfly optimization algorithm wireless sensor networks cluster head base station(BS)
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A Distributed Ant Colony Optimization Applied in Edge Detection
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作者 Min Chen 《Journal of Computer and Communications》 2024年第8期161-173,共13页
With the rise of image data and increased complexity of tasks in edge detection, conventional artificial intelligence techniques have been severely impacted. To be able to solve even greater problems of the future, le... With the rise of image data and increased complexity of tasks in edge detection, conventional artificial intelligence techniques have been severely impacted. To be able to solve even greater problems of the future, learning algorithms must maintain high speed and accuracy through economical means. Traditional edge detection approaches cannot detect edges in images in a timely manner due to memory and computational time constraints. In this work, a novel parallelized ant colony optimization technique in a distributed framework provided by the Hadoop/Map-Reduce infrastructure is proposed to improve the edge detection capabilities. Moreover, a filtering technique is applied to reduce the noisy background of images to achieve significant improvement in the accuracy of edge detection. Close examinations of the implementation of the proposed algorithm are discussed and demonstrated through experiments. Results reveal high classification accuracy and significant improvements in speedup, scaleup and sizeup compared to the standard algorithms. 展开更多
关键词 Distributed System ant Colony optimization Edge Detection MAPREDUCE SPEEDUP
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An Effective Optimization Algorithm for Ant Colony Vehicular Congestion Management
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作者 Tebepah Tariuge Timadi Matthew 《Journal of Computer and Communications》 2024年第9期119-130,共12页
Adaptability and dynamicity are special properties of social insects derived from the decentralized behavior of the insects. Authors have come up with designs for software solution that can regulate traffic congestion... Adaptability and dynamicity are special properties of social insects derived from the decentralized behavior of the insects. Authors have come up with designs for software solution that can regulate traffic congestion in a network transportation environment. The effectiveness of various researches on traffic management has been verified through appropriate metrics. Most of the traffic management systems are centered on using sensors, visual monitoring and neural networks to check for available parking space with the aim of informing drivers beforehand to prevent traffic congestion. There has been limited research on solving ongoing traffic congestion in congestion prone areas like car park with any of the common methods mentioned. This study focus however is on a motor park, as a highly congested area when it comes to traffic. The car park has two entrance gate and three exit gates which is divided into three Isle of parking lot where cars can park. An ant colony optimization algorithm (ACO) was developed as an effective management system for controlling navigation and vehicular traffic congestion problems when cars exit a motor park. The ACO based on the nature and movement of the natural ants, simulates the movement of cars out of the car park through their nearest choice exit. A car park simulation was also used for the mathematical computation of the pheromone. The system was implemented using SIMD because of its dual parallelization ability. The result showed about 95% increase on the number of vehicles that left the motor park in one second. A clear indication that pheromones are large determinants of the shortest route to take as cars followed the closest exit to them. Future researchers may consider monitoring a centralized tally system for cars coming into the park through a censored gate being. 展开更多
关键词 ant Colony optimization ADAPTABILITY CONGESTION PHEROMONES
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Security Test Case Prioritization through Ant Colony Optimization Algorithm
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作者 Abdulaziz Attaallah Khalil al-Sulbi +5 位作者 Areej Alasiry Mehrez Marzougui Mohd Waris Khan Mohd Faizan Alka Agrawal Dhirendra Pandey 《Computer Systems Science & Engineering》 SCIE EI 2023年第12期3165-3195,共31页
Security testing is a critical concern for organizations worldwide due to the potential financial setbacks and damage to reputation caused by insecure software systems.One of the challenges in software security testin... Security testing is a critical concern for organizations worldwide due to the potential financial setbacks and damage to reputation caused by insecure software systems.One of the challenges in software security testing is test case prioritization,which aims to reduce redundancy in fault occurrences when executing test suites.By effectively applying test case prioritization,both the time and cost required for developing secure software can be reduced.This paper proposes a test case prioritization technique based on the Ant Colony Optimization(ACO)algorithm,a metaheuristic approach.The performance of the ACO-based technique is evaluated using the Average Percentage of Fault Detection(APFD)metric,comparing it with traditional techniques.It has been applied to a Mobile Payment Wallet application to validate the proposed approach.The results demonstrate that the proposed technique outperforms the traditional techniques in terms of the APFD metric.The ACO-based technique achieves an APFD of approximately 76%,two percent higher than the second-best optimal ordering technique.These findings suggest that metaheuristic-based prioritization techniques can effectively identify the best test cases,saving time and improving software security overall. 展开更多
关键词 CONFIDENTIALITY INTEGRITY AUTHENTICATION NON-REPUDIATION RESILIENCE AUTHORIZATION ant Colony optimization algorithm
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Energy Efficient Networks Using Ant Colony Optimization with Game Theory Clustering
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作者 Harish Gunigari S.Chitra 《Intelligent Automation & Soft Computing》 SCIE 2023年第3期3557-3571,共15页
Real-time applications based on Wireless Sensor Network(WSN)tech-nologies quickly lead to the growth of an intelligent environment.Sensor nodes play an essential role in distributing information from networking and it... Real-time applications based on Wireless Sensor Network(WSN)tech-nologies quickly lead to the growth of an intelligent environment.Sensor nodes play an essential role in distributing information from networking and its transfer to the sinks.The ability of dynamical technologies and related techniques to be aided by data collection and analysis across the Internet of Things(IoT)network is widely recognized.Sensor nodes are low-power devices with low power devices,storage,and quantitative processing capabilities.The existing system uses the Artificial Immune System-Particle Swarm Optimization method to mini-mize the energy and improve the network’s lifespan.In the proposed system,a hybrid Energy Efficient and Reliable Ant Colony Optimization(ACO)based on the Routing protocol(E-RARP)and game theory-based energy-efficient clus-tering algorithm(GEC)were used.E-RARP is a new Energy Efficient,and Reli-able ACO-based Routing Protocol for Wireless Sensor Networks.The suggested protocol provides communications dependability and high-quality channels of communication to improve energy.For wireless sensor networks,a game theo-ry-based energy-efficient clustering technique(GEC)is used,in which each sen-sor node is treated as a player on the team.The sensor node can choose beneficial methods for itself,determined by the length of idle playback time in the active phase,and then decide whether or not to rest.The proposed E-RARP-GEC improves the network’s lifetime and data transmission;it also takes a minimum amount of energy compared with the existing algorithms. 展开更多
关键词 ant colony optimization game theory wireless sensor network network lifetime routing protocol data transmission energy efficiency
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A Scheme Library-Based Ant Colony Optimization with 2-Opt Local Search for Dynamic Traveling Salesman Problem
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作者 Chuan Wang Ruoyu Zhu +4 位作者 Yi Jiang Weili Liu Sang-Woon Jeon Lin Sun Hua Wang 《Computer Modeling in Engineering & Sciences》 SCIE EI 2023年第5期1209-1228,共20页
The dynamic traveling salesman problem(DTSP)is significant in logistics distribution in real-world applications in smart cities,but it is uncertain and difficult to solve.This paper proposes a scheme library-based ant... The dynamic traveling salesman problem(DTSP)is significant in logistics distribution in real-world applications in smart cities,but it is uncertain and difficult to solve.This paper proposes a scheme library-based ant colony optimization(ACO)with a two-optimization(2-opt)strategy to solve the DTSP efficiently.The work is novel and contributes to three aspects:problemmodel,optimization framework,and algorithmdesign.Firstly,in the problem model,traditional DTSP models often consider the change of travel distance between two nodes over time,while this paper focuses on a special DTSP model in that the node locations change dynamically over time.Secondly,in the optimization framework,the ACO algorithm is carried out in an offline optimization and online application framework to efficiently reuse the historical information to help fast respond to the dynamic environment.The framework of offline optimization and online application is proposed due to the fact that the environmental change inDTSPis caused by the change of node location,and therefore the newenvironment is somehowsimilar to certain previous environments.This way,in the offline optimization,the solutions for possible environmental changes are optimized in advance,and are stored in a mode scheme library.In the online application,when an environmental change is detected,the candidate solutions stored in the mode scheme library are reused via ACO to improve search efficiency and reduce computational complexity.Thirdly,in the algorithm design,the ACO cooperates with the 2-opt strategy to enhance search efficiency.To evaluate the performance of ACO with 2-opt,we design two challenging DTSP cases with up to 200 and 1379 nodes and compare them with other ACO and genetic algorithms.The experimental results show that ACO with 2-opt can solve the DTSPs effectively. 展开更多
关键词 Dynamic traveling salesman problem(DTSP) offline optimization and online application ant colony optimization(ACO) two-optimization(2-opt)strategy
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An Efficient Allocation for Lung Transplantation Using Ant Colony Optimization
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作者 Lina M.K.Al-Ebbini 《Intelligent Automation & Soft Computing》 SCIE 2023年第2期1971-1985,共15页
A relationship between lung transplant success and many features of recipients’/donors has long been studied.However,modeling a robust model of a potential impact on organ transplant success has proved challenging.In... A relationship between lung transplant success and many features of recipients’/donors has long been studied.However,modeling a robust model of a potential impact on organ transplant success has proved challenging.In this study,a hybrid feature selection model was developed based on ant colony opti-mization(ACO)and k-nearest neighbor(kNN)classifier to investigate the rela-tionship between the most defining features of recipients/donors and lung transplant success using data from the United Network of Organ Sharing(UNOS).The proposed ACO-kNN approach explores the features space to identify the representative attributes and classify patients’functional status(i.e.,quality of life)after lung transplantation.The efficacy of the proposed model was verified using 3,684 records and 118 input features from the UNOS.The developed approach examined the reliability and validity of the lung allocation process.The results are promising regarding accuracy prediction to be 91.3%and low computational time,along with better decision capabilities,emphasizing the potential for automatic classification of the lung and other organs allocation pro-cesses.In addition,the proposed model recommends a new perspective on how medical experts and clinicians respond to uncertain and challenging lung alloca-tion strategies.Having such ACO-kNN model,a medical professional can sum-marize information through the proposed method and make decisions for the upcoming transplants to allocate the donor organ. 展开更多
关键词 ant colony optimization(ACO) lung transplantation feature subset selection quality of life(QoL)
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Improved Ant Colony Optimization and Machine Learning Based Ensemble Intrusion Detection Model
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作者 S.Vanitha P.Balasubramanie 《Intelligent Automation & Soft Computing》 SCIE 2023年第4期849-864,共16页
Internet of things(IOT)possess cultural,commercial and social effect in life in the future.The nodes which are participating in IOT network are basi-cally attracted by the cyber-attack targets.Attack and identification... Internet of things(IOT)possess cultural,commercial and social effect in life in the future.The nodes which are participating in IOT network are basi-cally attracted by the cyber-attack targets.Attack and identification of anomalies in IoT infrastructure is a growing problem in the IoT domain.Machine Learning Based Ensemble Intrusion Detection(MLEID)method is applied in order to resolve the drawback by minimizing malicious actions in related botnet attacks on Message Queue Telemetry Transport(MQTT)and Hyper-Text Transfer Proto-col(HTTP)protocols.The proposed work has two significant contributions which are a selection of features and detection of attacks.New features are chosen from Improved Ant Colony Optimization(IACO)in the feature selection,and then the detection of attacks is carried out based on a combination of their possible proper-ties.The IACO approach is focused on defining the attacker’s important features against HTTP and MQTT.In the IACO algorithm,the constant factor is calculated against HTTP and MQTT based on the mean function for each element.Attack detection,the performance of several machine learning models are Distance Deci-sion Tree(DDT),Adaptive Neuro-Fuzzy Inference System(ANFIS)and Mahala-nobis Distance Support Vector Machine(MDSVM)were compared with predicting accurate attacks on the IoT network.The outcomes of these classifiers are combined into the ensemble model.The proposed MLEID strategy has effec-tively established malicious incidents.The UNSW-NB15 dataset is used to test the MLEID technique using data from simulated IoT sensors.Besides,the pro-posed MLEID technique has a greater detection rate and an inferior rate of false-positive compared to other conventional techniques. 展开更多
关键词 Network intrusion detection system(NIDS) internet of things(IOT) ensemble learning statisticalflow features BOTNET ensemble technique improved ant colony optimization(IACO) feature selection
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Route Search Method for Railway Replacement Buses Adopting Ant Colony Optimization
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作者 Kei Nagaoka Kayoko Yamamoto 《Journal of Geographic Information System》 2023年第4期391-420,共30页
In recent years, Japan, and especially rural areas have faced the growing problems of debt-ridden local railway lines along with the population decline and aging population. Therefore, it is best to consider the disco... In recent years, Japan, and especially rural areas have faced the growing problems of debt-ridden local railway lines along with the population decline and aging population. Therefore, it is best to consider the discontinuation of local railway lines and introduce replacement buses to secure the transportation methods of the local people especially in rural areas. Based on the above background, targeting local railway lines that may be discontinued in the near future, appropriate bus stops when provided with potential bus stops were selected, the present study proposed a method that introduces routes for railway replacement buses adopting ant colony optimization (ACO). The improved ACO was designed and developed based on the requirements set concerning the route length, number of turns, road width, accessibility of railway lines and zones without bus stops as well as the constraint conditions concerning the route length, number of turns and zones without bus stops. Original road network data were generated and processed adopting a geographic information systems (GIS), and these are used to search for the optimal route for railway replacement buses adopting the improved ACO concerning the 8 zones on the target railway line (JR Kakogawa line). By comparing the improved ACO with Dijkstra’s algorithm, its relevance was verified and areas needing further improvements were revealed. 展开更多
关键词 Local Railway Line Railway Replacement Bus Route Search Method ant Colony optimization (ACO) Dijkstra’s Algorithm Geographic Information Systems (GIS)
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MOALG: A Metaheuristic Hybrid of Multi-Objective Ant Lion Optimizer and Genetic Algorithm for Solving Design Problems
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作者 Rashmi Sharma Ashok Pal +4 位作者 Nitin Mittal Lalit Kumar Sreypov Van Yunyoung Nam Mohamed Abouhawwash 《Computers, Materials & Continua》 SCIE EI 2024年第3期3489-3510,共22页
This study proposes a hybridization of two efficient algorithm’s Multi-objective Ant Lion Optimizer Algorithm(MOALO)which is a multi-objective enhanced version of the Ant Lion Optimizer Algorithm(ALO)and the Genetic ... This study proposes a hybridization of two efficient algorithm’s Multi-objective Ant Lion Optimizer Algorithm(MOALO)which is a multi-objective enhanced version of the Ant Lion Optimizer Algorithm(ALO)and the Genetic Algorithm(GA).MOALO version has been employed to address those problems containing many objectives and an archive has been employed for retaining the non-dominated solutions.The uniqueness of the hybrid is that the operators like mutation and crossover of GA are employed in the archive to update the solutions and later those solutions go through the process of MOALO.A first-time hybrid of these algorithms is employed to solve multi-objective problems.The hybrid algorithm overcomes the limitation of ALO of getting caught in the local optimum and the requirement of more computational effort to converge GA.To evaluate the hybridized algorithm’s performance,a set of constrained,unconstrained test problems and engineering design problems were employed and compared with five well-known computational algorithms-MOALO,Multi-objective Crystal Structure Algorithm(MOCryStAl),Multi-objective Particle Swarm Optimization(MOPSO),Multi-objective Multiverse Optimization Algorithm(MOMVO),Multi-objective Salp Swarm Algorithm(MSSA).The outcomes of five performance metrics are statistically analyzed and the most efficient Pareto fronts comparison has been obtained.The proposed hybrid surpasses MOALO based on the results of hypervolume(HV),Spread,and Spacing.So primary objective of developing this hybrid approach has been achieved successfully.The proposed approach demonstrates superior performance on the test functions,showcasing robust convergence and comprehensive coverage that surpasses other existing algorithms. 展开更多
关键词 Multi-objective optimization genetic algorithm ant lion optimizer METAHEURISTIC
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Research on Grid Planning of Dual Power Distribution Network Based on Parallel Ant Colony Optimization Algorithm
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作者 Shuaixiang Wang 《Journal of Electronic Research and Application》 2023年第1期32-41,共10页
A distribution network plays an extremely important role in the safe and efficient operation of a power grid.As the core part of a power grid’s operation,a distribution network will have a significant impact on the s... A distribution network plays an extremely important role in the safe and efficient operation of a power grid.As the core part of a power grid’s operation,a distribution network will have a significant impact on the safety and reliability of residential electricity consumption.it is necessary to actively plan and modify the distribution network’s structure in the power grid,improve the quality of the distribution network,and optimize the planning of the distribution network,so that the network can be fully utilized to meet the needs of electricity consumption.In this paper,a distribution network grid planning algorithm based on the reliability of electricity consumption was completed using ant colony algorithm.For the distribution network structure planning of dual power sources,the parallel ant colony algorithm was used to prove that the premise of parallelism is the interactive process of ant colonies,and the dual power distribution network structure model is established based on the principle of the lowest cost.The artificial ants in the algorithm were compared with real ants in nature,and the basic steps and working principle of the ant colony optimization algorithm was studied with the help of the travelling salesman problem(TSP).Then,the limitations of the ant colony algorithm were analyzed,and an improvement strategy was proposed by using python for digital simulation.The results demonstrated the reliability of model-building and algorithm improvement. 展开更多
关键词 Parallel ant colony optimization algorithm Dual power sources Distribution network Grid planning
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Improved ant colony optimization for multi-depot heterogeneous vehicle routing problem with soft time windows 被引量:10
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作者 汤雅连 蔡延光 杨期江 《Journal of Southeast University(English Edition)》 EI CAS 2015年第1期94-99,共6页
Considering that the vehicle routing problem (VRP) with many extended features is widely used in actual life, such as multi-depot, heterogeneous types of vehicles, customer service priority and time windows etc., a ... Considering that the vehicle routing problem (VRP) with many extended features is widely used in actual life, such as multi-depot, heterogeneous types of vehicles, customer service priority and time windows etc., a mathematical model for multi-depot heterogeneous vehicle routing problem with soft time windows (MDHVRPSTW) is established. An improved ant colony optimization (IACO) is proposed for solving this model. First, MDHVRPSTW is transferred into different groups according to the nearest principle, and then the initial route is constructed by the scanning algorithm (SA). Secondly, genetic operators are introduced, and crossover probability and mutation probability are adaptively adjusted in order to improve the global search ability of the algorithm. Moreover, the smooth mechanism is used to improve the performance of the ant colony optimization (ACO). Finally, the 3-opt strategy is used to improve the local search ability. The proposed IACO was tested on three new instances that were generated randomly. The experimental results show that IACO is superior to the other three existing algorithms in terms of convergence speed and solution quality. Thus, the proposed method is effective and feasible, and the proposed model is meaningful. 展开更多
关键词 vehicle routing problem soft time window improved ant colony optimization customer service priority genetic algorithm
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Research on global path planning based on ant colony optimization for AUV 被引量:6
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作者 王宏健 熊伟 《Journal of Marine Science and Application》 2009年第1期58-64,共7页
Path planning is an important issue for autonomous underwater vehicles (AUVs) traversing an unknown environment such as a sea floor, a jungle, or the outer celestial planets. For this paper, global path planning usi... Path planning is an important issue for autonomous underwater vehicles (AUVs) traversing an unknown environment such as a sea floor, a jungle, or the outer celestial planets. For this paper, global path planning using large-scale chart data was studied, and the principles of ant colony optimization (ACO) were applied. This paper introduced the idea of a visibility graph based on the grid workspace model. It also brought a series of pheromone updating rules for the ACO planning algorithm. The operational steps of the ACO algorithm are proposed as a model for a global path planning method for AUV. To mimic the process of smoothing a planned path, a cutting operator and an insertion-point operator were designed. Simulation results demonstrated that the ACO algorithm is suitable for global path planning. The system has many advantages, including that the operating path of the AUV can be quickly optimized, and it is shorter, safer, and smoother. The prototype system successfully demonstrated the feasibility of the concept, proving it can be applied to surveys of unstructured unmanned environments. 展开更多
关键词 autonomous underwater vehicle (AUV) path planning ant colony optimization pathsmoothing
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Feature Extraction of Stored-grain Insects Based on Ant Colony Optimization and Support Vector Machine Algorithm 被引量:1
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作者 胡玉霞 张红涛 +1 位作者 罗康 张恒源 《Agricultural Science & Technology》 CAS 2012年第2期457-459,共3页
[Objective] The aim was to study the feature extraction of stored-grain insects based on ant colony optimization and support vector machine algorithm, and to explore the feasibility of the feature extraction of stored... [Objective] The aim was to study the feature extraction of stored-grain insects based on ant colony optimization and support vector machine algorithm, and to explore the feasibility of the feature extraction of stored-grain insects. [Method] Through the analysis of feature extraction in the image recognition of the stored-grain insects, the recognition accuracy of the cross-validation training model in support vector machine (SVM) algorithm was taken as an important factor of the evaluation principle of feature extraction of stored-grain insects. The ant colony optimization (ACO) algorithm was applied to the automatic feature extraction of stored-grain insects. [Result] The algorithm extracted the optimal feature subspace of seven features from the 17 morphological features, including area and perimeter. The ninety image samples of the stored-grain insects were automatically recognized by the optimized SVM classifier, and the recognition accuracy was over 95%. [Conclusion] The experiment shows that the application of ant colony optimization to the feature extraction of grain insects is practical and feasible. 展开更多
关键词 Stored-grain insects ant colony optimization algorithm Support vector machine Feature extraction RECOGNITION
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Improved ant colony optimization algorithm for the traveling salesman problems 被引量:22
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作者 Rongwei Gan Qingshun Guo +1 位作者 Huiyou Chang Yang Yi 《Journal of Systems Engineering and Electronics》 SCIE EI CSCD 2010年第2期329-333,共5页
Ant colony optimization (ACO) is a new heuristic algo- rithm which has been proven a successful technique and applied to a number of combinatorial optimization problems. The traveling salesman problem (TSP) is amo... Ant colony optimization (ACO) is a new heuristic algo- rithm which has been proven a successful technique and applied to a number of combinatorial optimization problems. The traveling salesman problem (TSP) is among the most important combinato- rial problems. An ACO algorithm based on scout characteristic is proposed for solving the stagnation behavior and premature con- vergence problem of the basic ACO algorithm on TSP. The main idea is to partition artificial ants into two groups: scout ants and common ants. The common ants work according to the search manner of basic ant colony algorithm, but scout ants have some differences from common ants, they calculate each route's muta- tion probability of the current optimal solution using path evaluation model and search around the optimal solution according to the mutation probability. Simulation on TSP shows that the improved algorithm has high efficiency and robustness. 展开更多
关键词 ant colony optimization heuristic algorithm scout ants path evaluation model traveling salesman problem.
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Max-Min Adaptive Ant Colony Optimization Approach to Multi-UAVs Coordinated Trajectory Replanning in Dynamic and Uncertain Environments 被引量:33
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作者 Hai-bin Duan,Xiang-yin Zhang,Jiang Wu,Guan-jun MaSchool of Automation Science and Electrical Engineering,Beihang University,Beijing 100191,P.R.China 《Journal of Bionic Engineering》 SCIE EI CSCD 2009年第2期161-173,共13页
Multiple Uninhabited Aerial Vehicles (multi-UAVs) coordinated trajectory replanning is one of the most complicated global optimum problems in multi-UAVs coordinated control. Based on the construction of the basic mode... Multiple Uninhabited Aerial Vehicles (multi-UAVs) coordinated trajectory replanning is one of the most complicated global optimum problems in multi-UAVs coordinated control. Based on the construction of the basic model of multi-UAVs coordinated trajectory replanning, which includes problem description, threat modeling, constraint conditions, coordinated function and coordination mechanism, a novel Max-Min adaptive Ant Colony Optimization (ACO) approach is presented in detail. In view of the characteristics of multi-UAVs coordinated trajectory replanning in dynamic and uncertain environments, the minimum and maximum pheromone trails in ACO are set to enhance the searching capability, and the point pheromone is adopted to achieve the collision avoidance between UAVs at the trajectory planner layer. Considering the simultaneous arrival and the air-space collision avoidance, an Estimated Time of Arrival (ETA) is decided first. Then the trajectory and flight velocity of each UAV are determined. Simulation experiments are performed under the complicated combating environment containing some static threats and popup threats. The results demonstrate the feasibility and the effectiveness of the proposed approach. 展开更多
关键词 Multiple Uninhabited Aerial Vehicles (multi-UAVs) ant Colony optimization (ACO) trajectory replanning collision avoidance Estimated Time of Arrival (ETA)
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Novel Approach to Nonlinear PID Parameter Optimization Using Ant Colony Optimization Algorithm 被引量:11
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作者 Duan Hai-bin Wang Dao-bo Yu Xiu-fen 《Journal of Bionic Engineering》 SCIE EI CSCD 2006年第2期73-78,共6页
This paper presents an application of an Ant Colony Optimization (ACO) algorithm to optimize the parameters in the design of a type of nonlinear PID controller. The ACO algorithm is a novel heuristic bionic algorith... This paper presents an application of an Ant Colony Optimization (ACO) algorithm to optimize the parameters in the design of a type of nonlinear PID controller. The ACO algorithm is a novel heuristic bionic algorithm, which is based on the behaviour of real ants in nature searching for food. In order to optimize the parameters of the nonlinear PID controller using ACO algorithm, an objective function based on position tracing error was constructed, and elitist strategy was adopted in the improved ACO algorithm. Detailed simulation steps are presented. This nonlinear PID controller using the ACO algorithm has high precision of control and quick response. 展开更多
关键词 ant Colony optimization ALGORITHM PHEROMONE nonlinear PID parameter optimization
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Hybrid ant colony optimization for the resource-constrained project scheduling problem 被引量:10
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作者 Linyi Deng Yan Lin Ming Chen 《Journal of Systems Engineering and Electronics》 SCIE EI CSCD 2010年第1期67-71,共5页
To solve the resource-constrained project scheduling problem (RCPSP), a hybrid ant colony optimization (HACO) approach is presented. To improve the quality of the schedules, the HACO is incorporated with an extend... To solve the resource-constrained project scheduling problem (RCPSP), a hybrid ant colony optimization (HACO) approach is presented. To improve the quality of the schedules, the HACO is incorporated with an extended double justification in which the activity splitting is applied to predict whether the schedule could be improved. The HACO is tested on the set of large benchmark problems from the project scheduling problem library (PSPLIB). The computational result shows that the proposed algo- rithm can improve the quality of the schedules efficiently. 展开更多
关键词 project scheduling double justification ant colony optimization activity splitting.
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