Applying particle swarm optimization and honey bee mating optimization in developing an intelligent market segmentation system

2010 ◽  
Vol 19 (2) ◽  
pp. 182-191 ◽  
Author(s):  
Chui-Yu Chiu ◽  
I-Ting Kuo
Keyword(s):  
Particle Swarm Optimization ◽  
Honey Bee ◽  
Market Segmentation ◽  
Particle Swarm ◽  
Swarm Optimization ◽  
Honey Bee Mating Optimization

scholarly journals Using Particle Swarm Optimization, Genetic Algorithm, Honey Bee mating Optimization and Shuffle Frog Leaping Algorithm for solving OPF Problem with their Comparison

2015 ◽  
Vol 15 (3) ◽  
pp. 445
Author(s):  
Sajjad Ahmadnia ◽  
Ehsan Tafehi
Keyword(s):  
Genetic Algorithm ◽  
Particle Swarm Optimization ◽  
Honey Bee ◽  
Power Flow ◽  
Particle Swarm ◽  
Test System ◽  
Mathematical Methods ◽  
Swarm Optimization ◽  
Shuffle Frog Leaping Algorithm ◽  
Honey Bee Mating Optimization

Today using evolutionary programing for solving complex, nonlinear mathematical problems like optimum power flow is commonly in use. These types of problems are naturally nonlinear and the conventional mathematical methods aren’t powerful enough for achieving the desirable results. In this study an Optimum Power Flow problem solved by means of minimization of fuel costs for IEEE 30 buses test system by Particle Swarm Optimization (PSO), Genetic Algorithm (GA), Honey Bee Mating Optimization (HBMO) and Shuffle Frog Leaping Algorithm (SFLA), these algorithms has been used in MATLAB medium with help of MATHPOWER to achieving more precise results and comparing these results with the other proposed results in other published papers.

An intelligent market segmentation system using k-means and particle swarm optimization

2009 ◽  
Vol 36 (3) ◽  
pp. 4558-4565 ◽  
Author(s):  
Chui-Yu Chiu ◽  
Yi-Feng Chen ◽  
I-Ting Kuo ◽  
He Chun Ku
Keyword(s):  
Particle Swarm Optimization ◽  
Market Segmentation ◽  
Particle Swarm ◽  
Swarm Optimization

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

Sign in / Sign up

Export Citation Format

Share Document