scholarly journals Multi-Threshold Image Segmentation of Maize Diseases Based on Elite Comprehensive Particle Swarm Optimization and Otsu

2021 ◽  
Vol 12 ◽  
Author(s):  
Chengcheng Chen ◽  
Xianchang Wang ◽  
Ali Asghar Heidari ◽  
Helong Yu ◽  
Huiling Chen
Keyword(s):  
Particle Swarm Optimization ◽  
Particle Swarm ◽  
Animal Husbandry ◽  
Raw Material ◽  
Light Industry ◽  
Swarm Optimization ◽  
Noise Interference ◽  
Elite Strategy ◽  
Single Method ◽  
Stable Yield

Maize is a major global food crop and as one of the most productive grain crops, it can be eaten; it is also a good feed for the development of animal husbandry and essential raw material for light industry, chemical industry, medicine, and health. Diseases are the main factor limiting the high and stable yield of maize. Scientific and practical identification is a vital link to reduce the damage of diseases and accurate segmentation of disease spots is one of the fundamental techniques for disease identification. However, one single method cannot achieve a good segmentation effect to meet the diversity and complexity of disease spots. In order to solve the shortcomings of noise interference and oversegmentation in the Otsu segmentation method, a non-local mean filtered two-dimensional histogram was used to remove the noise in disease images and a new elite strategy improved comprehensive particle swarm optimization (PSO) method was used to find the optimal segmentation threshold of the objective function in this study. The experimental results of segmenting three kinds of maize foliar disease images show that the segmentation effect of this method is better than other similar algorithms and it has better convergence and stability.

Application of Optimization Algorithm for Ceramic Formulations

2012 ◽  
Vol 518-523 ◽  
pp. 3938-3942
Author(s):  
Xiang Cheng ◽  
Su Li Wang ◽  
Shu Wu Cheng
Keyword(s):  
Particle Swarm Optimization ◽  
Chemical Composition ◽  
Optimization Algorithm ◽  
Simplex Method ◽  
Raw Materials ◽  
Optimization Algorithms ◽  
Particle Swarm ◽  
Ceramic Materials ◽  
Raw Material ◽  
Swarm Optimization

ceramic formula; genetic algorithm; the simplex method; particle swarm optimization Abstract. The composition of ceramic materials determines its performance, so ceramic formula determines the performance of the product. The factors, such as supply of local raw materials, chemical composition, and raw material price, determine the formula. Selecting raw materials from a variety of raw materials which are consistent with the target formula and the percentage content of the chemical composition is a very important job. The chemical composition in the ceramic material is various, and the traditional method is to calculate the formula by hand. The workload is very heavy, and the results are often not accurate, and don’t meet actual requirements. By minimizing the deviation of chemical composition and mineral ingredients, the chemical and comprehensive formula optimization models of ceramic preform body were constructed. Several typical optimization algorithms were used to construct the formula model, such as particle swarm optimization, genetic algorithms and Nelder-Mead simplex method. The experimental results were presented and compared and it concluded that the particle swarm optimization algorithm is superior to the other optimization algorithms.

scholarly journals Solving Bilevel Multiobjective Programming Problem by Elite Quantum Behaved Particle Swarm Optimization

2012 ◽  
Vol 2012 ◽  
pp. 1-20 ◽  
Author(s):  
Tao Zhang ◽  
Tiesong Hu ◽  
Jia-wei Chen ◽  
Zhongping Wan ◽  
Xuning Guo
Keyword(s):  
Particle Swarm Optimization ◽  
Programming Problem ◽  
Multiobjective Programming ◽  
Particle Swarm ◽  
Test Problems ◽  
Swarm Optimization ◽  
Pareto Optimal Front ◽  
Elite Strategy ◽  
Low Dimension ◽  
Multiobjective Programming Problem

An elite quantum behaved particle swarm optimization (EQPSO) algorithm is proposed, in which an elite strategy is exerted for the global best particle to prevent premature convergence of the swarm. The EQPSO algorithm is employed for solving bilevel multiobjective programming problem (BLMPP) in this study, which has never been reported in other literatures. Finally, we use eight different test problems to measure and evaluate the proposed algorithm, including low dimension and high dimension BLMPPs, as well as attempt to solve the BLMPPs whose theoretical Pareto optimal front is not known. The experimental results show that the proposed algorithm is a feasible and efficient method for solving BLMPPs.

An Improved Particle Swarm Optimization Algorithm for Integrated Scheduling Model in AGV-Served Manufacturing Systems

2018 ◽  
Vol 17 (03) ◽  
pp. 375-390 ◽  
Author(s):  
Fuqiang Zhang ◽  
Jingjing Li
Keyword(s):  
Particle Swarm Optimization ◽  
Optimization Algorithm ◽  
Manufacturing Systems ◽  
Particle Swarm Optimization Algorithm ◽  
Particle Swarm ◽  
Automated Guided Vehicles ◽  
Raw Material ◽  
Swarm Optimization ◽  
Improved Particle Swarm Optimization ◽  
Scheduling Model

To address the resources optimization problem of AGV-served manufacturing systems driven by multi-varieties and small-batch production orders, a scheduling model integrating machines and automated guided vehicles (AGVs) is proposed. In this model, the makespan of jobs from raw material storage to finished parts storage through multi-stage processes has been used as the objective function, and the utilization ratios of machines and AGVs have been used as the comprehensive evaluation functions. An improved particle swarm optimization algorithm with the characteristics of main particles and nested particles is developed to solve a reasonable scheduling scheme. Compared with the basic particle swarm optimization algorithm and genetic algorithm, the numerical result suggests that the nested particle swarm optimization algorithm has more advantages in convergence and solving efficiency. It is expected that this study can provide a useful reference for the flexible adjustment of AGV-served manufacturing systems.

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
Sign in / Sign up

Export Citation Format

Share Document