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International Conference on Computing, Power and Communication Technologies (GUCON)

作者： Pandey, Ashish Rajan, Abhishek Nandi, Arnab Natl Inst Technol Silchar Dept Elect & Commun Engn Cachar 788010 Assam India Natl Inst Technol Sikkim Dept Elect & Elect Engn Sikkim 737139 India

ISBN: (纸本)9781538644911

Wireless sensor networks (WSNs) are extensively used in numerous applications from sensing and tracking to atmospheric quantity measurement. Sensor nodes used in the network are mostly battery powered and due to odd terrain of deployment it is not always easy to replace them. For managing this battery resource of the WSNs various schemes have been proposed and implemented. In this paper we have used Moth Flame optimization algorithm in the clustering and routing for enhancing the lifetime of the sensor network. Outcome through this algorithm are compared with the previously used various algorithms like particle swarm optimization, Genetic algorithm and Least Distance Clustering algorithm.

关键词： WSN Moth flame optimization particle swarm optimization algorithm PSO Genetic algorithm Clustering Routing

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Research on Hospital Operation Index Prediction Method Based on PSO-Holt-Winters Model 18

Research on Hospital Operation Index Prediction Method Based...

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2nd International Conference on Computer Science and Application Engineering (CSAE)

作者： Zheng, Tao Zhang, Ya Fan, Chenhao Shanghai Jiao Tong Univ Tongren Hosp Inst Image Commun & Networking Shanghai Peoples R China Shanghai Jiao Tong Univ Inst Image Commun & Networking Shanghai Peoples R China Shanghai Jiao Tong Univ Tongren Hosp Shanghai Peoples R China

ISBN: (纸本)9781450365123

The prediction(1) of hospital operation indicators is of great significance and can provide an important basis for hospital operation and management, so as to assist managers to make decisions such as resource allocation and task planning. In order to solve this problem, a novel Holt-Winters model based on particle swarm optimization (PSO) is proposed, aiming at the accurate prediction of hospital operating indicators. In the process of model construction, according to the characteristics of time series data of hospital operation indicators, a time decay mean square error function is constructed as an optimization function of particle swarm optimization algorithm, which enables particle swarm optimization algorithm to better fit recent historical data and grasp the characteristics of recent time series, so as to improve the prediction accuracy. An example is given to analyze the hospital operation index data of a third-class hospital from 2014 to 2017. By initializing the parameters of the model and optimizing the parameters, the improved PSO-Holt-Winters model of TDMSE-1 is established, which can accurately predict the outpatient, inpatient, emergency, discharged and surgical cases.

关键词： Hospital operation indicators particle swarm optimization algorithm Holt - Winters model

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Improved Optimal Path algorithm Based on Ant Colony algorithm

Improved Optimal Path Algorithm Based on Ant Colony Algorith...

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2019 4th International Conference on Automatic Control and Mechatronic Engineering(ACME 2019)

作者： Zhaohua Long Ruifang Dong Chongqing University of Posts and Telecommunications

Ant colony algorithm is based on Ant System(AS) and it is a very important group intelligence algorithm,which is used in many fields,but there are also some *** classical ant colony algorithm is analyzed and studied,and an improved P-ACS algorithm is proposed based on ACS algorithm in this *** analysis and experiment,it is found that although the performance of ACS algorithm is higher than AS algorithm,there are still some problems,such as:falling into local optimal solution,search stagnation,and slow initial *** important reason for the above problems is that the pheromone update can not accurately reflect the actual situation of the *** at this problem,a P-ACS ant colony algorithm is proposed based on particle swarm optimization algorithm(PSO).The algorithm optimizes the pheromone update strategy from three aspects:pheromone concentration range setting,initial pheromone setting and global update strategy improvement.

关键词： Ant colony algorithm ACS particle swarm optimization algorithm P-ACS

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Short Term Load Forecasting and Early Warning of Charging Station Based on PSO-SVM

Short Term Load Forecasting and Early Warning of Charging St...

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作者： Liao Xiaoqun Kang Xiaofan Ma Li Cao Nanlan Xi’an University of Science & Technology

In this paper,a short-term load forecasting model and a load early warning model for charging station based on PSO-SVM are *** swarm optimization(PSO) is used to optimize the parameters of support vector machine(SVM) model,and the PSO-SVM load forecasting model for the optimal nuclear parameters of charging station is established according to the normalized root mean square error(NRMS).On the basis of it,a load warning model of charging station is established and verified by an *** show that the short-term load forecasting model based on PSO-SVM and the load forecasting model of charging station meet the requirements of forecasting and forecasting accuracy.

关键词： Load Forecasting Optimal Kernel Parameters Support Vector Machine particle swarm optimization algorithm Load Early Warning

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Establishing an emergency communication network and optimal path using multiple autonomous rover robots

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CONCURRENCY AND COMPUTATION-PRACTICE & EXPERIENCE 2019年第14期31卷

作者： Sakthitharan, S. Jayashri, S. Adhiparasakthi Engn Coll Fac Informat & Commun Engn Melmaruvathur 603319 India

The natural calamity or disaster may destroy all communication networks especially a cellular network that relies on a tower. Although many solutions to an ad hoc wireless network have been proposed, forming a network covering a respective region with mobile robots toward optimal coverage remains to be an open problem. In this paper, we take the initiative to handle the optimal network coverage and path selection in disaster region with the help of multiple movable/rover robots. This paper consists of load balance distribution algorithm and optimal coverage algorithm applied to find the next optimally possible node location for all robots. Next, the robots maneuvering in an unknown disaster environment to identify the optimal path between the source and destination by using a particle swarm optimization algorithm. Finally, simulated results show that the algorithms can significantly improve the network coverage in the entire region, and the optimal path can effectively identify the optimal solution for all rover robots.

关键词： autonomous maneuvering disaster management load balancing algorithm optimal coverage algorithm particle swarm optimization algorithm

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Performance Evaluation of optimization algorithms based on GPU using CUDA Architecture

Performance Evaluation of Optimization Algorithms based on G...

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5th IEEE Latin American Conference on Computational Intelligence (LA-CCI)

作者： Kawano, Yunkio Valdez, Fevrier Castillo, Oscar Tijuana Inst Technol Tijuana Baja California Mexico

ISBN: (纸本)9781538646267

In this paper, an evaluation was made on the performance of two of the most commonly used optimization algorithms, which are the particle swarm optimization (PSO) algorithm and the Genetic algorithm (GA). In this case, average time of execution is compared, executing the algorithm using the original code on the traditional processor against the modified algorithm where certain processes of the algorithm are integrated into the video card. These changes demonstrate a significant improvement in execution time. On the other hand a graphical interface was made for each one of the optimization algorithms to facilitate the process of handling the parameters.

关键词： particle swarm optimization algorithm Genetic algorithm CUDA Architecture

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Research on optimization Model of Power Grid Project Investment

Research on Optimization Model of Power Grid Project Investm...

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作者： Zifa Liu Dong Peng Xinyue Wang Hui Li Hongyang Liu North China Electric Power University State Grid Economic and Technological Research Institute CO.LTD

For the multi-criteria decision-making problem of portfolio investment in power grid construction projects,the modern portfolio theory is used to construct a portfolio optimization model based on adaptive particle swarm optimization algorithm under the constraints of power demand,grid enterprise investment capability and reliability level in this *** the shortcomings of slow convergence and long operation time of particle swarm optimization,the change of inertia weight is used to change the update mode of particle position,which improves the convergence speed of the *** portfolio optimization model provides an applicable decision-making method for complex grid optimization investment decisions,and builds a complex grid optimization investment decision management process based on power demand and investment capability.

关键词： optimization model power grid project investment particle swarm optimization algorithm

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Lifetime Enhancement of Wireless Sensor Networks by using Sine Cosine optimization algorithm 7

Lifetime Enhancement of Wireless Sensor Networks by using Si...

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7th International Conference on Reliability, Infocom Technologies and optimization - Trends and Future Directions (ICRITO)

作者： Pandey, Ashish Rajan, Abhishek Nandi, Arnab Natl Inst Technol Silchar Dept Elect & Commun Engn Cachar 788010 Assam India Natl Inst Technol Sikkim Dept Elect & Elect Engn Sikkim 737139 India

ISBN: (纸本)9781538646922

Wireless sensor networks (WSNs) have numerous applications from the measurements of atmospheric quantity, tracking to the various medical applications. Sensor nodes in the networks are mostly battery powered and they are having limited lifetime as it is not always feasible to replace their battery. To overcome this constraint of energy various energy management scheme have been proposed and implemented. In this paper we have used Sine Cosine algorithm (SCA) in routing and clustering for enhancing the working lifetime of WSNs. As the cluster head (CH) in the networks having more energy expenditure so we have also used some higher energy nodes to perform CHs operations for the enhancement of the lifetime. We have compared the results with the other algorithms namely particle swarm optimization (PSO) algorithm, genetic algorithm (GA) and least distance clustering (LDC) algorithm. Results are also have been studied after moving the base station at different locations.

关键词： WSNs Sine Cosine optimization algorithm Genetic algorithm particle swarm optimization algorithm PSO Clustering Routing

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Feature Selection for Cancer Classification Based on SRPSO algorithm

Feature Selection for Cancer Classification Based on SRPSO A...

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2019 International Conference on Informatics, Control and Robotics 2019信息学、控制和机器人学国际会议(ICICR2019)

作者： Qiu-lan XIAO Hong ZHENG Qing-an YAO School of Computer Science and Engineering Changchun University of Technology

Improving the accuracy of cancer classification plays an important role in cancer-assisted diagnosis. Genes selection is an important factor for improving the accuracy of cancer classification. In this paper, based on the standard particle swarm optimization algorithm, an SRPSO algorithm with self-adaptive and reverse-learning mechanism is proposed. It is applied to select feature genes from microarray datasets, and the results are used for cancer classification via SVM to make 5-fold cross-validation. To evaluate the performance of SRPSO, four different cancer datasets including Colon, ALLML, MLL, and SRBCT were selected. Based on the evaluation process, the SRPSO algorithm provided better results on each dataset.

关键词： Microarray gene data Cancer classification particle swarm optimization algorithm Self-adaptive Reverse-learning

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Research on the Influence of Aerobic Exercise on Athletes' Body Shape and Function Based on Multiple Linear Regression

Research on the Influence of Aerobic Exercise on Athletes' B...

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作者： Xiaojing Zhou Shanghai Lixin Accounting Institute of Finance

Cardiopulmonary Function Test of Athletes is the Key to Scientifically and Reasonably Formulate Training Plans. in Order to Solve the Problem of Large Errors in the Existing Cardiorespiratory Function Detection Methods, a Multiple Linear Regression Cardiorespiratory Function Detection Method Based on particle swarm optimization is Proposed. through Significant Difference Correlation Evaluation, the Metabolic Circulation Function in Sports is Analyzed to Realize Comprehensive Evaluation of Athletes' Absolute Strength, Speed Strength and Strength Endurance, and the Internal Relationship between Athletes' Aerobic Metabolism Ability and Anaerobic Metabolism Ability is Obtained. the Results Show That 3 Months Aerobic Exercise Can Obviously Improve the Body Shape and Physiological Function of Young Women. particle swarm optimization is Used to Optimize and Improve the Speed and Accuracy of Cardiopulmonary Function Detection. the Method Can Effectively Improve the Cardiopulmonary Function of Athletes Before and after Aerobic Training, and Has High Modeling Accuracy.

关键词： Multiple Linear Regression particle swarm optimization algorithm Aerobic Exercise Body Shape Function

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