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Hybrid Hierarchical Particle Swarm Optimization with Evolutionary Artificial Bee Colony Algorithm for Task Scheduling in Cloud Computing
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作者 Shasha Zhao Huanwen Yan +3 位作者 Qifeng Lin Xiangnan Feng He Chen Dengyin Zhang 《Computers, Materials & Continua》 SCIE EI 2024年第1期1135-1156,共22页
Task scheduling plays a key role in effectively managing and allocating computing resources to meet various computing tasks in a cloud computing environment.Short execution time and low load imbalance may be the chall... Task scheduling plays a key role in effectively managing and allocating computing resources to meet various computing tasks in a cloud computing environment.Short execution time and low load imbalance may be the challenges for some algorithms in resource scheduling scenarios.In this work,the Hierarchical Particle Swarm Optimization-Evolutionary Artificial Bee Colony Algorithm(HPSO-EABC)has been proposed,which hybrids our presented Evolutionary Artificial Bee Colony(EABC),and Hierarchical Particle Swarm Optimization(HPSO)algorithm.The HPSO-EABC algorithm incorporates both the advantages of the HPSO and the EABC algorithm.Comprehensive testing including evaluations of algorithm convergence speed,resource execution time,load balancing,and operational costs has been done.The results indicate that the EABC algorithm exhibits greater parallelism compared to the Artificial Bee Colony algorithm.Compared with the Particle Swarm Optimization algorithm,the HPSO algorithmnot only improves the global search capability but also effectively mitigates getting stuck in local optima.As a result,the hybrid HPSO-EABC algorithm demonstrates significant improvements in terms of stability and convergence speed.Moreover,it exhibits enhanced resource scheduling performance in both homogeneous and heterogeneous environments,effectively reducing execution time and cost,which also is verified by the ablation experimental. 展开更多
关键词 Cloud computing distributed processing evolutionary artificial bee colony algorithm hierarchical particle swarm optimization load balancing
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Unmanned wave glider heading model identification and control by artificial fish swarm algorithm 被引量:2
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作者 WANG Lei-feng LIAO Yu-lei +2 位作者 LI Ye ZHANG Wei-xin PAN Kai-wen 《Journal of Central South University》 SCIE EI CAS CSCD 2018年第9期2131-2142,共12页
We introduce the artificial fish swarm algorithm for heading motion model identification and control parameter optimization problems for the“Ocean Rambler”unmanned wave glider(UWG).First,under certain assumptions,th... We introduce the artificial fish swarm algorithm for heading motion model identification and control parameter optimization problems for the“Ocean Rambler”unmanned wave glider(UWG).First,under certain assumptions,the rigid-flexible multi-body system of the UWG was simplified as a rigid system composed of“thruster+float body”,based on which a planar motion model of the UWG was established.Second,we obtained the model parameters using an empirical method combined with parameter identification,which means that some parameters were estimated by the empirical method.In view of the specificity and importance of the heading control,heading model parameters were identified through the artificial fish swarm algorithm based on tank test data,so that we could take full advantage of the limited trial data to factually describe the dynamic characteristics of the system.Based on the established heading motion model,parameters of the heading S-surface controller were optimized using the artificial fish swarm algorithm.Heading motion comparison and maritime control experiments of the“Ocean Rambler”UWG were completed.Tank test results show high precision of heading motion prediction including heading angle and yawing angular velocity.The UWG shows good control performance in tank tests and sea trials.The efficiency of the proposed method is verified. 展开更多
关键词 unmanned wave glider artificial fish swarm algorithm heading model parameters identification control parameters optimization
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Optimization of Fairhurst-Cook Model for 2-D Wing Cracks Using Ant Colony Optimization (ACO), Particle Swarm Intelligence (PSO), and Genetic Algorithm (GA)
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作者 Mohammad Najjarpour Hossein Jalalifar 《Journal of Applied Mathematics and Physics》 2018年第8期1581-1595,共15页
The common failure mechanism for brittle rocks is known to be axial splitting which happens parallel to the direction of maximum compression. One of the mechanisms proposed for modelling of axial splitting is the slid... The common failure mechanism for brittle rocks is known to be axial splitting which happens parallel to the direction of maximum compression. One of the mechanisms proposed for modelling of axial splitting is the sliding crack or so called, “wing crack” model. Fairhurst-Cook model explains this specific type of failure which starts by a pre-crack and finally breaks the rock by propagating 2-D cracks under uniaxial compression. In this paper, optimization of this model has been considered and the process has been done by a complete sensitivity analysis on the main parameters of the model and excluding the trends of their changes and also their limits and “peak points”. Later on this paper, three artificial intelligence algorithms including Particle Swarm Intelligence (PSO), Ant Colony Optimization (ACO) and genetic algorithm (GA) has been used and compared in order to achieve optimized sets of parameters resulting in near-maximum or near-minimum amounts of wedging forces creating a wing crack. 展开更多
关键词 WING Crack Fairhorst-Cook Model Sensitivity Analysis OPTIMIZATION Particle swarm INTELLIGENCE (PSO) ant colony OPTIMIZATION (ACO) Genetic algorithm (GA)
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Codebook design using improved particle swarm optimization based on selection probability of artificial bee colony algorithm 被引量:2
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作者 浦灵敏 胡宏梅 《Journal of Chongqing University》 CAS 2014年第3期90-98,共9页
In the paper, a new selection probability inspired by artificial bee colony algorithm is introduced into standard particle swarm optimization by improving the global extremum updating condition to enhance the capabili... In the paper, a new selection probability inspired by artificial bee colony algorithm is introduced into standard particle swarm optimization by improving the global extremum updating condition to enhance the capability of its overall situation search. The experiment result shows that the new scheme is more valuable and effective than other schemes in the convergence of codebook design and the performance of codebook, and it can avoid the premature phenomenon of the particles. 展开更多
关键词 vector quantization codebook design particle swarm optimization artificial bee colony algorithm
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Service Composition Instantiation Based on Cross-Modified Artificial Bee Colony Algorithm
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作者 Lei Huo Zhiliang Wang 《China Communications》 SCIE CSCD 2016年第10期233-244,共12页
Internet of things(IoT) imposes new challenges on service composition as it is difficult to manage a quick instantiation of a complex services from a growing number of dynamic candidate services. A cross-modified Arti... Internet of things(IoT) imposes new challenges on service composition as it is difficult to manage a quick instantiation of a complex services from a growing number of dynamic candidate services. A cross-modified Artificial Bee Colony Algorithm(CMABC) is proposed to achieve the optimal solution services in an acceptable time and high accuracy. Firstly, web service instantiation model was established. What is more, to overcome the problem of discrete and chaotic solution space, the global optimal solution was used to accelerate convergence rate by imitating the cross operation of Genetic algorithm(GA). The simulation experiment result shows that CMABC exhibited faster convergence speed and better convergence accuracy than some other intelligent optimization algorithms. 展开更多
关键词 optimization of service composition optimal service instantiation artificial bee colony algorithm swarm algorithm cross strategy
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Development of an Artificial Fish Swarm Algorithm Based on aWireless Sensor Networks in a Hydrodynamic Background
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作者 Sheng Bai Feng Bao +1 位作者 Fengzhi Zhao Miaomiao Liu 《Fluid Dynamics & Materials Processing》 EI 2020年第5期935-946,共12页
The main objective of the present study is the development of a new algorithm that can adapt to complex and changeable environments.An artificial fish swarm algorithm is developed which relies on a wireless sensor net... The main objective of the present study is the development of a new algorithm that can adapt to complex and changeable environments.An artificial fish swarm algorithm is developed which relies on a wireless sensor network(WSN)in a hydrodynamic background.The nodes of this algorithm are viscous fluids and artificial fish,while related‘events’are directly connected to the food available in the related virtual environment.The results show that the total processing time of the data by the source node is 6.661 ms,of which the processing time of crosstalk data is 3.789 ms,accounting for 56.89%.The total processing time of the data by the relay node is 15.492 ms,of which the system scheduling and the Carrier Sense Multiple Access(CSMA)rollback time of the forwarding is 8.922 ms,accounting for 57.59%.The total time for the data processing of the receiving node is 11.835 ms,of which the processing time of crosstalk data is 3.791 ms,accounting for 32.02%;the serial data processing time is 4.542 ms,accounting for 38.36%.Crosstalk packets occupy a certain amount of system overhead in the internal communication of nodes,which is one of the causes of node-level congestion.We show that optimizing the crosstalk phenomenon can alleviate the internal congestion of nodes to some extent. 展开更多
关键词 artificial fish swarm algorithm wireless sensor network network measurement HYDRODYNAMICS
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Path Planning for AUVs Based on Improved APF-AC Algorithm 被引量:1
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作者 Guojun Chen Danguo Cheng +2 位作者 Wei Chen Xue Yang Tiezheng Guo 《Computers, Materials & Continua》 SCIE EI 2024年第3期3721-3741,共21页
With the increase in ocean exploration activities and underwater development,the autonomous underwater vehicle(AUV)has been widely used as a type of underwater automation equipment in the detection of underwater envir... With the increase in ocean exploration activities and underwater development,the autonomous underwater vehicle(AUV)has been widely used as a type of underwater automation equipment in the detection of underwater environments.However,nowadays AUVs generally have drawbacks such as weak endurance,low intelligence,and poor detection ability.The research and implementation of path-planning methods are the premise of AUVs to achieve actual tasks.To improve the underwater operation ability of the AUV,this paper studies the typical problems of path-planning for the ant colony algorithm and the artificial potential field algorithm.In response to the limitations of a single algorithm,an optimization scheme is proposed to improve the artificial potential field ant colony(APF-AC)algorithm.Compared with traditional ant colony and comparative algorithms,the APF-AC reduced the path length by 1.57%and 0.63%(in the simple environment),8.92%and 3.46%(in the complex environment).The iteration time has been reduced by approximately 28.48%and 18.05%(in the simple environment),18.53%and 9.24%(in the complex environment).Finally,the improved APF-AC algorithm has been validated on the AUV platform,and the experiment is consistent with the simulation.Improved APF-AC algorithm can effectively reduce the underwater operation time and overall power consumption of the AUV,and shows a higher safety. 展开更多
关键词 PATH-PLANNING autonomous underwater vehicle ant colony algorithm artificial potential field bio-inspired neural network
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Artificial Fish Swarm Optimization with Deep Learning Enabled Opinion Mining Approach 被引量:1
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作者 Saud S.Alotaibi Eatedal Alabdulkreem +5 位作者 Sami Althahabi Manar Ahmed Hamza Mohammed Rizwanullah Abu Sarwar Zamani Abdelwahed Motwakel Radwa Marzouk 《Computer Systems Science & Engineering》 SCIE EI 2023年第4期737-751,共15页
Sentiment analysis or opinion mining(OM)concepts become familiar due to advances in networking technologies and social media.Recently,massive amount of text has been generated over Internet daily which makes the patte... Sentiment analysis or opinion mining(OM)concepts become familiar due to advances in networking technologies and social media.Recently,massive amount of text has been generated over Internet daily which makes the pattern recognition and decision making process difficult.Since OM find useful in business sectors to improve the quality of the product as well as services,machine learning(ML)and deep learning(DL)models can be considered into account.Besides,the hyperparameters involved in the DL models necessitate proper adjustment process to boost the classification process.Therefore,in this paper,a new Artificial Fish Swarm Optimization with Bidirectional Long Short Term Memory(AFSO-BLSTM)model has been developed for OM process.The major intention of the AFSO-BLSTM model is to effectively mine the opinions present in the textual data.In addition,the AFSO-BLSTM model undergoes pre-processing and TF-IFD based feature extraction process.Besides,BLSTM model is employed for the effectual detection and classification of opinions.Finally,the AFSO algorithm is utilized for effective hyperparameter adjustment process of the BLSTM model,shows the novelty of the work.A complete simulation study of the AFSO-BLSTM model is validated using benchmark dataset and the obtained experimental values revealed the high potential of the AFSO-BLSTM model on mining opinions. 展开更多
关键词 Sentiment analysis opinion mining natural language processing artificial fish swarm algorithm deep learning
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A Review on Representative Swarm Intelligence Algorithms for Solving Optimization Problems:Applications and Trends 被引量:41
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作者 Jun Tang Gang Liu Qingtao Pan 《IEEE/CAA Journal of Automatica Sinica》 SCIE EI CSCD 2021年第10期1627-1643,共17页
Swarm intelligence algorithms are a subset of the artificial intelligence(AI)field,which is increasing popularity in resolving different optimization problems and has been widely utilized in various applications.In th... Swarm intelligence algorithms are a subset of the artificial intelligence(AI)field,which is increasing popularity in resolving different optimization problems and has been widely utilized in various applications.In the past decades,numerous swarm intelligence algorithms have been developed,including ant colony optimization(ACO),particle swarm optimization(PSO),artificial fish swarm(AFS),bacterial foraging optimization(BFO),and artificial bee colony(ABC).This review tries to review the most representative swarm intelligence algorithms in chronological order by highlighting the functions and strengths from 127 research literatures.It provides an overview of the various swarm intelligence algorithms and their advanced developments,and briefly provides the description of their successful applications in optimization problems of engineering fields.Finally,opinions and perspectives on the trends and prospects in this relatively new research domain are represented to support future developments. 展开更多
关键词 ant colony optimization(ACO) artificial bee colony(ABC) artificial fish swarm(AFS) bacterial foraging optimization(BFO) optimization particle swarm optimization(PSO) swarm intelligence
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Improved artificial bee colony algorithm with mutual learning 被引量:7
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作者 Yu Liu Xiaoxi Ling +1 位作者 Yu Liang Guanghao Liu 《Journal of Systems Engineering and Electronics》 SCIE EI CSCD 2012年第2期265-275,共11页
The recently invented artificial bee colony (ABC) al- gorithm is an optimization algorithm based on swarm intelligence that has been used to solve many kinds of numerical function optimization problems. It performs ... The recently invented artificial bee colony (ABC) al- gorithm is an optimization algorithm based on swarm intelligence that has been used to solve many kinds of numerical function optimization problems. It performs well in most cases, however, there still exists an insufficiency in the ABC algorithm that ignores the fitness of related pairs of individuals in the mechanism of find- ing a neighboring food source. This paper presents an improved ABC algorithm with mutual learning (MutualABC) that adjusts the produced candidate food source with the higher fitness between two individuals selected by a mutual learning factor. The perfor- mance of the improved MutualABC algorithm is tested on a set of benchmark functions and compared with the basic ABC algo- rithm and some classical versions of improved ABC algorithms. The experimental results show that the MutualABC algorithm with appropriate parameters outperforms other ABC algorithms in most experiments. 展开更多
关键词 artificial bee colony (ABC) algorithm numerical func- tion optimization swarm intelligence mutual learning.
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Traveling Salesman Problem Using an Enhanced Hybrid Swarm Optimization Algorithm 被引量:2
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作者 郑建国 伍大清 周亮 《Journal of Donghua University(English Edition)》 EI CAS 2014年第3期362-367,共6页
The traveling salesman problem( TSP) is a well-known combinatorial optimization problem as well as an NP-complete problem. A dynamic multi-swarm particle swarm optimization and ant colony optimization( DMPSO-ACO) was ... The traveling salesman problem( TSP) is a well-known combinatorial optimization problem as well as an NP-complete problem. A dynamic multi-swarm particle swarm optimization and ant colony optimization( DMPSO-ACO) was presented for TSP.The DMPSO-ACO combined the exploration capabilities of the dynamic multi-swarm particle swarm optimizer( DMPSO) and the stochastic exploitation of the ant colony optimization( ACO) for solving the traveling salesman problem. In the proposed hybrid algorithm,firstly,the dynamic swarms,rapidity of the PSO was used to obtain a series of sub-optimal solutions through certain iterative times for adjusting the initial allocation of pheromone in ACO. Secondly,the positive feedback and high accuracy of the ACO were employed to solving whole problem. Finally,to verify the effectiveness and efficiency of the proposed hybrid algorithm,various scale benchmark problems were tested to demonstrate the potential of the proposed DMPSO-ACO algorithm. The results show that DMPSO-ACO is better in the search precision,convergence property and has strong ability to escape from the local sub-optima when compared with several other peer algorithms. 展开更多
关键词 particle swarm optimization(PSO) ant colony optimization(ACO) swarm intelligence TRAVELING SALESMAN problem(TSP) hybrid algorithm
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Evolutionary Algorithms in Software Defined Networks: Techniques, Applications, and Issues 被引量:1
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作者 LIAO Lingxia Victor C.M.Leung LAI Chin-Feng 《ZTE Communications》 2017年第3期20-36,共17页
A software defined networking(SDN) system has a logically centralized control plane that maintains a global network view and enables network-wide management, optimization, and innovation. Network-wide management and o... A software defined networking(SDN) system has a logically centralized control plane that maintains a global network view and enables network-wide management, optimization, and innovation. Network-wide management and optimization problems are typicallyvery complex with a huge solution space, large number of variables, and multiple objectives. Heuristic algorithms can solve theseproblems in an acceptable time but are usually limited to some particular problem circumstances. On the other hand, evolutionaryalgorithms(EAs), which are general stochastic algorithms inspired by the natural biological evolution and/or social behavior of species, can theoretically be used to solve any complex optimization problems including those found in SDNs. This paper reviewsfour types of EAs that are widely applied in current SDNs: Genetic Algorithms(GAs), Particle Swarm Optimization(PSO), Ant Colony Optimization(ACO), and Simulated Annealing(SA) by discussing their techniques, summarizing their representative applications, and highlighting their issues and future works. To the best of our knowledge, our work is the first that compares the tech-niques and categorizes the applications of these four EAs in SDNs. 展开更多
关键词 SDN evolutionary algorithms Genetic algorithms Particle swarm Optimization ant colony Optimization Simulated Annealing
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Approach to WTA in air combat using IAFSA-IHS algorithm 被引量:11
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作者 LI Zhanwu CHANG Yizhe +3 位作者 KOU Yingxin YANG Haiyan XU An LI You 《Journal of Systems Engineering and Electronics》 SCIE EI CSCD 2018年第3期519-529,共11页
In this paper, a static weapon target assignment(WTA)problem is studied. As a critical problem in cooperative air combat,outcome of WTA directly influences the battle. Along with the cost of weapons rising rapidly, ... In this paper, a static weapon target assignment(WTA)problem is studied. As a critical problem in cooperative air combat,outcome of WTA directly influences the battle. Along with the cost of weapons rising rapidly, it is indispensable to design a target assignment model that can ensure minimizing targets survivability and weapons consumption simultaneously. Afterwards an algorithm named as improved artificial fish swarm algorithm-improved harmony search algorithm(IAFSA-IHS) is proposed to solve the problem. The effect of the proposed algorithm is demonstrated in numerical simulations, and results show that it performs positively in searching the optimal solution and solving the WTA problem. 展开更多
关键词 air combat weapon target assignment improved artificial fish swarm algorithm-improved harmony search algorithm(IAFSA-IHS) artificial fish swarm algorithm(AFSA) harmony search(HS)
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Blackboard Mechanism Based Ant Colony Theory for Dynamic Deployment of Mobile Sensor Networks 被引量:5
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作者 Guang-ping Qi Ping Song Ke-jie Li 《Journal of Bionic Engineering》 SCIE EI CSCD 2008年第3期197-203,共7页
A novel bionic swarm intelligence algorithm, called ant colony algorithm based on a blackboard mechanism, is proposed to solve the autonomy and dynamic deployment of mobiles sensor networks effectively. A blackboard m... A novel bionic swarm intelligence algorithm, called ant colony algorithm based on a blackboard mechanism, is proposed to solve the autonomy and dynamic deployment of mobiles sensor networks effectively. A blackboard mechanism is introduced into the system for making pheromone and completing the algorithm. Every node, which can be looked as an ant, makes one information zone in its memory for communicating with other nodes and leaves pheromone, which is created by ant itself in naalre. Then ant colony theory is used to find the optimization scheme for path planning and deployment of mobile Wireless Sensor Network (WSN). We test the algorithm in a dynamic and unconfigurable environment. The results indicate that the algorithm can reduce the power consumption by 13% averagely, enhance the efficiency of path planning and deployment of mobile WSN by 15% averagely. 展开更多
关键词 ant colony algorithm wireless sensor network blackboard mechanism bionic swarm intelligence algorithm
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Improved algorithms to plan missions for agile earth observation satellites 被引量:3
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作者 Huicheng Hao Wei Jiang Yijun Li 《Journal of Systems Engineering and Electronics》 SCIE EI CSCD 2014年第5期811-821,共11页
This study concentrates of the new generation of the agile (AEOS). AEOS is a key study object on management problems earth observation satellite in many countries because of its many advantages over non-agile satell... This study concentrates of the new generation of the agile (AEOS). AEOS is a key study object on management problems earth observation satellite in many countries because of its many advantages over non-agile satellites. Hence, the mission planning and scheduling of AEOS is a popular research problem. This research investigates AEOS characteristics and establishes a mission planning model based on the working principle and constraints of AEOS as per analysis. To solve the scheduling issue of AEOS, several improved algorithms are developed. Simulation results suggest that these algorithms are effective. 展开更多
关键词 mission planning immune clone algorithm hybrid genetic algorithm (EA) improved ant colony algorithm general particle swarm optimization (PSO) agile earth observation satellite (AEOS).
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Ant Colony Optimization with Potential Field Based on Grid Map for Mobile Robot Path Planning 被引量:4
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作者 陈国良 刘杰 张钏钏 《Journal of Donghua University(English Edition)》 EI CAS 2016年第5期764-767,共4页
For the mobile robot path planning under the complex environment,ant colony optimization with artificial potential field based on grid map is proposed to avoid traditional ant colony algorithm's poor convergence a... For the mobile robot path planning under the complex environment,ant colony optimization with artificial potential field based on grid map is proposed to avoid traditional ant colony algorithm's poor convergence and local optimum.Firstly,the pheromone updating mechanism of ant colony is designed by a hybrid strategy of global map updating and local grids updating.Then,some angles between the vectors of artificial potential field and the orientations of current grid are introduced to calculate the visibility of eight-neighbor cells of cellular automata,which are adopted as ant colony's inspiring factor to calculate the transition probability based on the pseudo-random transition rule cellular automata.Finally,mobile robot dynamic path planning and the simulation experiments are completed by this algorithm,and the experimental results show that the method is feasible and effective. 展开更多
关键词 colony visibility automata colony robot neighbor updating Robot obstacles consuming
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IWD-Miner: A Novel Metaheuristic Algorithm for Medical Data Classification
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作者 Sarab AlMuhaideb Reem BinGhannam +3 位作者 Nourah Alhelal Shatha Alduheshi Fatimah Alkhamees Raghad Alsuhaibani 《Computers, Materials & Continua》 SCIE EI 2021年第2期1329-1346,共18页
Medical data classification(MDC)refers to the application of classification methods on medical datasets.This work focuses on applying a classification task to medical datasets related to specific diseases in order to ... Medical data classification(MDC)refers to the application of classification methods on medical datasets.This work focuses on applying a classification task to medical datasets related to specific diseases in order to predict the associated diagnosis or prognosis.To gain experts’trust,the prediction and the reasoning behind it are equally important.Accordingly,we confine our research to learn rule-based models because they are transparent and comprehensible.One approach to MDC involves the use of metaheuristic(MH)algorithms.Here we report on the development and testing of a novel MH algorithm:IWD-Miner.This algorithm can be viewed as a fusion of Intelligent Water Drops(IWDs)and AntMiner+.It was subjected to a four-stage sensitivity analysis to optimize its performance.For this purpose,21 publicly available medical datasets were used from the Machine Learning Repository at the University of California Irvine.Interestingly,there were only limited differences in performance between IWDMiner variants which is suggestive of its robustness.Finally,using the same 21 datasets,we compared the performance of the optimized IWD-Miner against two extant algorithms,AntMiner+and J48.The experiments showed that both rival algorithms are considered comparable in the effectiveness to IWD-Miner,as confirmed by the Wilcoxon nonparametric statistical test.Results suggest that IWD-Miner is more efficient than AntMiner+as measured by the average number of fitness evaluations to a solution(1,386,621.30 vs.2,827,283.88 fitness evaluations,respectively).J48 exhibited higher accuracy on average than IWD-Miner(79.58 vs.73.65,respectively)but produced larger models(32.82 leaves vs.8.38 terms,respectively). 展开更多
关键词 ant colony optimization antMiner+ IWDs IWD-Miner J48 medical data classification metaheuristic algorithms swarm intelligence
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SVC Video Transmission Optimization Algorithm in Software Defined Network
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作者 Zhe Liu 《China Communications》 SCIE CSCD 2018年第10期143-149,共7页
Scalable video coding(SVC) is a powerful tool to solve the network heterogeneity and terminal diversity in video applications. However, in related works about the optimization of SVC-based video streaming over Softwar... Scalable video coding(SVC) is a powerful tool to solve the network heterogeneity and terminal diversity in video applications. However, in related works about the optimization of SVC-based video streaming over Software Defined Network(SDN), most of the them are focused either on the number of transmission layers or on the optimization of transmission path for specific layer. In this paper, we propose a noval optimization algorithm for SVC to dynamically adjust the number of layers and optimize the transmission paths simultaneously. We establish the problem model based on the 0/1 knapsack model, and then solve it with Artificial Fish Swarm Algorithm. Additionally, the simulations are carried out on the Mininet platform, which show that our approach can dynamically adjust the number of layers and select the optimal paths at the same time. As a result, it can achieve an effective allocation of network resources which mitigates the congestion and reduces the loss of non-SVC stream. 展开更多
关键词 SVC SDN OpenFlow Mininet artificial fish swarm algorithm (AFSA) 0/1 knapsack model
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A Novel Approach Based on Hybrid Algorithm for Energy Efficient Cluster Head Identification in Wireless Sensor Networks
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作者 C.Ram Kumar K.Murali Krishna +3 位作者 Mohammad Shabbir Alam K.Vigneshwaran Sridharan Kannan C.Bharatiraja 《Computer Systems Science & Engineering》 SCIE EI 2022年第10期259-273,共15页
The Wireless Sensor Networks(WSN)is a self-organizing network with random deployment of wireless nodes that connects each other for effective monitoring and data transmission.The clustering technique employed to group... The Wireless Sensor Networks(WSN)is a self-organizing network with random deployment of wireless nodes that connects each other for effective monitoring and data transmission.The clustering technique employed to group the collection of nodes for data transmission and each node is assigned with a cluster head.The major concern with the identification of the cluster head is the consideration of energy consumption and hence this paper proposes an hybrid model which forms an energy efficient cluster head in the Wireless Sensor Network.The proposed model is a hybridization of Glowworm Swarm Optimization(GSO)and Artificial Bee Colony(ABC)algorithm for the better identification of cluster head.The performance of the proposed model is compared with the existing techniques and an energy analysis is performed and is proved to be more efficient than the existing model with normalized energy of 5.35%better value and reduction of time complexity upto 1.46%.Above all,the proposed model is 16%ahead of alive node count when compared with the existing methodologies. 展开更多
关键词 Wireless sensor network CLUSTER cluster head hybrid model glowworm swarm optimization artificial bee colony algorithm energy consumption
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Algorithms for the Optimization of Well Placements—A Comparative Study
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作者 Stella Unwana Udoeyop Innocent Oseribho Oboh Maurice Oscar Afiakinye 《Advances in Chemical Engineering and Science》 2018年第2期101-111,共11页
The Artificial Bee Colony (ABC) is one of the numerous stochastic algorithms for optimization that has been written for solving constrained and unconstrained optimization problems. This novel optimization algorithm is... The Artificial Bee Colony (ABC) is one of the numerous stochastic algorithms for optimization that has been written for solving constrained and unconstrained optimization problems. This novel optimization algorithm is very efficient and as promising as it is;it can be favourably compared to other optimization algorithms and in some cases, it has been proven to be better than some known algorithms (like Particle Swarm Optimization (PSO)), especially when used in Well placement optimization problems that can be encountered in the Petroleum industry. In this paper, the ABC algorithm has been modified to improve its speed and convergence in finding the optimum solution to a well placement optimization problem. The effects of variations of the control parameters for both algorithms were studied, as well as the algorithms’ performances in the cases studied. The modified ABC (MABC) algorithm gave better results than the Artificial Bee Colony algorithm. It was noticed that the performance of the ABC algorithm increased with increase in the number of its optimization agents for both algorithms studied. The modified ABC algorithm overcame the challenge posed by the use of uniformly generated random numbers with very rough NPV surface. This new modified ABC algorithm proposed in this work will be a great tool in optimization for the Petroleum industry as it involves Well placements for optimum oil production. 展开更多
关键词 artificial BEE colony OPTIMIZATION WELL PLACEMENT Stochastic algorithm Particle swarm OPTIMIZATION
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