scholarly journals A CAD System for the Detection of Abnormalities in the Mammograms Using the Metaheuristic Algorithm Particle Swarm Optimization (PSO)

2016 ◽  
pp. 505-517 ◽  
Author(s):  
Khaoula Belhaj Soulami ◽  
Mohamed Nabil Saidi ◽  
Ahmed Tamtaoui
Keyword(s):  
Particle Swarm Optimization ◽  
Particle Swarm ◽  
Metaheuristic Algorithm ◽  
Swarm Optimization ◽  
Cad System

scholarly journals Resolving the optimal selection of a natural reserve using the particle swarm optimisation by applying transfer functions

2018 ◽  
Author(s):  
Boris Almonacid
Keyword(s):  
Particle Swarm Optimization ◽  
Transfer Functions ◽  
Particle Swarm ◽  
Pso Algorithm ◽  
Optimal Selection ◽  
Metaheuristic Algorithm ◽  
Swarm Optimization ◽  
Natural Reserve ◽  
Transfer Method ◽  
Selection Of

The optimal selection of a natural reserve (OSRN) is an optimisation problem with a binary domain. To solve this problem the metaheuristic algorithm Particle Swarm Optimization (PSO) has been chosen. The PSO algorithm has been designed to solve problems in real domains. Therefore, a transfer method has been applied that converts the equations with real domains of the PSO algorithm into binary results that are compatible with the OSRN problem. Four transfer functions have been tested in four case studies to solve the OSRN problem. According to the tests carried out, it is concluded that two of the four transfer functions are apt to solve the problem of optimal selection of a natural reserve.

scholarly journals Refined Heuristic Swarm Intelligence Algorithm

2020 ◽  
pp. 267-275
Author(s):  
I. I. Aina ◽  
C. N. Ejieji
Keyword(s):  
Particle Swarm Optimization ◽  
Swarm Intelligence ◽  
Particle Swarm ◽  
Pso Algorithm ◽  
Metaheuristic Algorithm ◽  
Swarm Optimization ◽  
Inertia Weight ◽  
Intelligence Algorithm ◽  
Swarm Intelligence Algorithm

In this paper, a new metaheuristic algorithm named refined heuristic intelligence swarm (RHIS) algorithm is developed from an existing particle swarm optimization (PSO) algorithm by introducing a disturbing term to the velocity of PSO and modifying the inertia weight, in which the comparison between the two algorithms is also addressed.

scholarly journals Resolving the optimal selection of a natural reserve using the particle swarm optimisation by applying transfer functions

2018 ◽  
Author(s):  
Boris L Almonacid
Keyword(s):  
Particle Swarm Optimization ◽  
Transfer Functions ◽  
Particle Swarm ◽  
Pso Algorithm ◽  
Optimal Selection ◽  
Metaheuristic Algorithm ◽  
Swarm Optimization ◽  
Natural Reserve ◽  
Transfer Method ◽  
Selection Of

The optimal selection of a natural reserve (OSRN) is an optimisation problem with a binary domain. To solve this problem the metaheuristic algorithm Particle Swarm Optimization (PSO) has been chosen. The PSO algorithm has been designed to solve problems in real domains. Therefore, a transfer method has been applied that converts the equations with real domains of the PSO algorithm into binary results that are compatible with the OSRN problem. Four transfer functions have been tested in four case studies to solve the OSRN problem. According to the tests carried out, it is concluded that two of the four transfer functions are apt to solve the problem of optimal selection of a natural reserve.

scholarly journals Sparse Based Particle Swarm Optimization Algorithm

2021 ◽  
Author(s):  
Lalit Kumar ◽  
Manish Pandey ◽  
Mitul Kumar Ahirwal
Keyword(s):  
Particle Swarm Optimization ◽  
Particle Swarm Optimization Algorithm ◽  
Particle Swarm ◽  
Computation Time ◽  
Pso Algorithm ◽  
Computational Time ◽  
Metaheuristic Algorithm ◽  
Objective Functions ◽  
Swarm Optimization ◽  
Almost All

Abstract Particle Swarm Optimization (PSO) is the well-known metaheuristic algorithm for optimization, inspired from swarm of species.PSO can be used in various problems solving related to engineering and science inclusive of but not restricted to increase the heat transfer of systems, to diagnose the health problem using PSO based on microscopic imaging. One of the limitations with Standard-PSO and other swarm based algorithms is large computational time as position vectors are dense. In this study, a sparse initialization based PSO (Sparse-PSO) algorithm has been proposed. Comparison of proposed Sparse-PSO with Standard-PSO has been done through evaluation over several standard benchmark objective functions. Our proposed Sparse-PSO method takes less computation time and provides better solution for almost all benchmark objective functions as compared to Standard-PSO method.

scholarly journals Resolving the optimal selection of a natural reserve using the particle swarm optimisation by applying transfer functions

2018 ◽  
Author(s):  
Boris Almonacid
Keyword(s):  
Particle Swarm Optimization ◽  
Transfer Functions ◽  
Particle Swarm ◽  
Pso Algorithm ◽  
Optimal Selection ◽  
Metaheuristic Algorithm ◽  
Swarm Optimization ◽  
Natural Reserve ◽  
Transfer Method ◽  
Selection Of

The optimal selection of a natural reserve (OSRN) is an optimisation problem with a binary domain. To solve this problem the metaheuristic algorithm Particle Swarm Optimization (PSO) has been chosen. The PSO algorithm has been designed to solve problems in real domains. Therefore, a transfer method has been applied that converts the equations with real domains of the PSO algorithm into binary results that are compatible with the OSRN problem. Four transfer functions have been tested in four case studies to solve the OSRN problem. According to the tests carried out, it is concluded that two of the four transfer functions are apt to solve the problem of optimal selection of a natural reserve.

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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