Fuzzy Particle Swarm Optimization for Physician Scheduling

2015 ◽  
pp. 103-118
Keyword(s):  
Particle Swarm Optimization ◽  
Particle Swarm ◽  
Swarm Optimization ◽  
Physician Scheduling

scholarly journals Multi-Objective Physician Scheduling using Native Binary Particle Swarm Optimization and Its Variance

2019 ◽  
Vol 8 (4) ◽  
pp. 5230-5243
Keyword(s):  
Mathematical Model ◽  
Particle Swarm Optimization ◽  
Fitness Function ◽  
Particle Swarm ◽  
Metaheuristic Algorithms ◽  
Binary Particle Swarm Optimization ◽  
Swarm Optimization ◽  
The Government ◽  
The Mathematical Model ◽  
Physician Scheduling

Physician Scheduling is one of the crucial operational activities in hospitals. The requirements of physician schedules involve numbers of physicians of different types, constraints from the physicians themselves and multiple regulations from the government and the hospital. Those requirements contribute to the complexity of physician scheduling model which makes it interesting to experiment and to solve. There have been many solutions proposed by researchers, particularly using metaheuristic algorithms, to automate physicians scheduling. Learning from the existing research results, this research experiments two metaheuristic algorithms which are considered simple, robust and effective to solve physician scheduling problem, i.e. Native Binary Particle Swarm Optimization (Native BPSO) with Sigmoid Transformation and its variance. The variation is done by removing particles’ local best positions from the velocity equation, with the intention to allow particles to move quickly towards global best position. The experiments were started from conducting the literature review of metaheuristic algorithms, followed by collecting physician schedules data from the selected hospital, designing the mathematical model of physician scheduling, developing and modifying the algorithms, testing and evaluating the results and at last, concluding the outcomes. Outcomes of the experiments comprise of three items, i.e. the mathematical model, the recommended algorithm and the best parameters’ values to be applied for the selected algorithm. Based on the experiments, the Native BPSO variant turns out to produce better result in terms of its fitness function and the number of days assigned for each physician in the schedule. Considering that the model, the algorithm and the parameters’ values have been implemented in a web-based application, it is ready for use by the selected hospital.

Smart teaching mode based on particle swarm image recognition and human-computer interaction deep learning

2020 ◽  
Vol 39 (4) ◽  
pp. 5699-5711
Author(s):  
Shirong Long ◽  
Xuekong Zhao
Keyword(s):  
Feature Extraction ◽  
Particle Swarm Optimization ◽  
Deep Learning ◽  
Real Time ◽  
Image Recognition ◽  
Particle Swarm ◽  
Learning Technology ◽  
Search Performance ◽  
Swarm Optimization ◽  
Teaching Mode

The smart teaching mode overcomes the shortcomings of traditional teaching online and offline, but there are certain deficiencies in the real-time feature extraction of teachers and students. In view of this, this study uses the particle swarm image recognition and deep learning technology to process the intelligent classroom video teaching image and extracts the classroom task features in real time and sends them to the teacher. In order to overcome the shortcomings of the premature convergence of the standard particle swarm optimization algorithm, an improved strategy for multiple particle swarm optimization algorithms is proposed. In order to improve the premature problem in the search performance algorithm of PSO algorithm, this paper combines the algorithm with the useful attributes of other algorithms to improve the particle diversity in the algorithm, enhance the global search ability of the particle, and achieve effective feature extraction. The research indicates that the method proposed in this paper has certain practical effects and can provide theoretical reference for subsequent related research.

Sistem Kontrol Optimal Fuzzy-Particle Swarm Optimization

2018 ◽  
Vol 9 (1) ◽  
pp. 1-5
Author(s):  
Fachrudin Hunaini ◽  
Imam Robandi ◽  
Nyoman Sutantra
Keyword(s):  
Fuzzy Logic ◽  
Particle Swarm Optimization ◽  
Control System ◽  
Membership Function ◽  
Fuzzy Logic Control ◽  
Particle Swarm ◽  
Optimization Method ◽  
Swarm Optimization ◽  
Motion Error ◽  
Logic Control

Fuzzy Logic Control (FLC) is a reliable control system for controlling nonlinear systems, but to obtain optimal fuzzy logic control results, optimal Membership Function parameters are needed. Therefore in this paper Particle Swarm Optimization (PSO) is used as a fast and accurate optimization method to determine Membership Function parameters. The optimal control system simulation is carried out on the automatic steering system of the vehicle model and the results obtained are the vehicle's lateral motion error can be minimized so that the movement of the vehicle can always be maintained on the expected trajectory

Adaptive Inertia Weight Particle Swarm Optimization For Linear and Nonlinear Channel Equalization

2012 ◽  
Vol 3 (4) ◽  
pp. 1-4
Author(s):  
Diana D.C Diana D.C ◽  
◽  
Joy Vasantha Rani.S.P Joy Vasantha Rani.S.P ◽  
Nithya.T.R Nithya.T.R ◽  
Srimukhee.B Srimukhee.B
Keyword(s):  
Particle Swarm Optimization ◽  
Particle Swarm ◽  
Channel Equalization ◽  
Swarm Optimization ◽  
Inertia Weight ◽  
Linear And Nonlinear
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