A New Fitness Function in Binary Particle Swarm Optimization for Efficient Design of Frequency Selective Surfaces

2018 ◽  
Author(s):  
Dae-Do Yang ◽  
Chan-Sun Park ◽  
Jong-Gwan Yook
Keyword(s):  
Particle Swarm Optimization ◽  
Fitness Function ◽  
Particle Swarm ◽  
Binary Particle Swarm Optimization ◽  
Frequency Selective Surfaces ◽  
Efficient Design ◽  
Swarm Optimization ◽  
Frequency Selective

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.

Study of Simulation Test Generation Method Based on Improved Binary Particle Swarm Optimization Algorithm

2013 ◽  
Vol 823 ◽  
pp. 661-664
Author(s):  
Guang Yao Lian ◽  
Peng Cheng Yan ◽  
Jiang Sheng Sun ◽  
Kao Li Huang
Keyword(s):  
Particle Swarm Optimization ◽  
Optimization Algorithm ◽  
Test Generation ◽  
Particle Swarm Optimization Algorithm ◽  
Fitness Function ◽  
Particle Swarm ◽  
Optimal Solution ◽  
Binary Particle Swarm Optimization ◽  
Simulation Test ◽  
Swarm Optimization

To solve the backdating problem of traditional test generation methods, it puts forward a new test generation method based on improved binary particle swarm optimization algorithm in the paper. It estates the fitness function of test vector and faults in the circuits, and the optimal solution is the maximal value of the function. The experimentations prove that the method can reduce the compute quantity of test generation.

An Optimal Binary Particle Swarm Optimization Based Feature Selection Model for Big Data Analysis of Product Assessment

2021 ◽  
Vol 18 (4) ◽  
pp. 1233-1238
Author(s):  
R. Sathya ◽  
L. R. Aravind Babu
Keyword(s):  
Decision Making ◽  
Feature Selection ◽  
Big Data ◽  
Particle Swarm Optimization ◽  
Fitness Function ◽  
Particle Swarm ◽  
Experimental Result ◽  
Binary Particle Swarm Optimization ◽  
Product Reviews ◽  
Swarm Optimization

Big data defines the state where the size, speed and kind of data go beyond a memory or executing capabilities for precise and timely decision-making. Big data analytics is integrated with ML and statistical methods for processing big data and recognizes the important data. At present times, the generation of online product reviews has exponentially increased at each and every second. These applications have resulted in developing the volumes of data which can be used for prediction and classification for decision making process. Compared with other models, various techniques are applied in solving the big data problem, feature selection (FS) is known to be an efficient method. FS operations could be exploring with the application of a subset of features which is related to the topic of précised definition of the existing datasets. Deplorably, search using this type of sub-sets results in the problems of combinatorial as well as maximum time consuming. The meta-heuristic approaches are typically employed to facilitate the choice of features. This paper presents an optimal extreme learning machine (ELM) based binary particle swarm optimization to precede the FS process. The proposed method develops a Fitness Function (FF) by applying ELM. And the best solution of the FF has been explored under the application of BPSO technique. For instance, the dataset of product review which are derived from Amazon including synthetic data, which is comprised with total of 235,000 positive and 147,000 negative review records is used. The experimental result implied that the ELM-BPSO technique is comparably best

Sign in / Sign up

Export Citation Format

Share Document