Particle Swam Optimization Algorithm Research Articles

Effect of argon on classification and identification of organics based on laser‐induced breakdown spectroscopy

AbstractOrganic matter is the material basis of life, which is widely present in people's products and lives. The rapid and accurate detection of organic matter can be applied to the identification of fake and inferior products, the traceability of the origin of agricultural products, and the early warning of explosives in antiterrorism. By combining laser‐induced breakdown spectroscopy (LIBS) with argon, the spectral characteristics of five common organic compounds (nitroglycerin, metronidazole, polyformaldehyde, polypropylene, and phenolic resin), the plasma lifetime, the parameters of plasma thermodynamic state, and the distribution of three existing forms of the element C in two gas environments were compared, the influence of argon on organic LIBS results and the reasons for its enhancement were analyzed. The results indicate that argon makes the atomization of carbon chain structures in organic matter more complete, and reduce the interference of the element N in air on the organic LIBS results to a certain extent. The intensity differences of different organic substances are more obvious. support vector machine optimized by principal component analysis and particle swam optimization algorithm was used to classify five kinds of organic matter. The prediction accuracy of classification in air is 94.4%, and it improves to 100% in argon. This result provides a powerful method for high‐precision, real‐time in‐situ, and rapid identification of organic substances, which has important scientific significance for the study of organic substances LIBS.

Brownian motion based multi-objective particle swarm optimization methodology and application in binary classification

The particle swam optimization (PSO) method has been widely applied in evolutionary computation and related industrial areas. However, the optimization performance of traditional PSO is usually influenced by particle updating displacement and velocity, which is randomly distributed and makes the solution solving process inefficient. To deal with this challenge, a Brownian particle motion and Euler-Maruyama (EM) methodology-based model is proposed to improve the performance of the multi-objective PSO. The EM based PSO principles and algorithm framework are introduced by theoretical analysis and mathematical modeling, and then the optimization performance of four comparing PSO models is tested, while the feasibility of the new PSO4 is verified by typical benchmark problems. Afterwards, the characterizations of the EM based PSO method are investigated from the perspective of particle motion mechanism analysis, binary classification application and various optimization performance comparison. The result manifests that the new PSO4 features higher accuracy and less running time among 24 related optimization methods for 4 typical standard datasets of Ionosphere, Breast, Diabetes and Wine. Finally, the multi-objective optimization and classification application potential of the Brownian motion and EM based PSO4 is demonstrated, and the future work regarding the new PSO model is discussed.

Effectiveness of Several Metaheuristic Methods to Analyze Hydraulic Parameters in a Drinking Water Distribution Network

The reliability and ease of applying metaheuristic methods in solving large and complex equation systems make it interesting to be applied as an alternative solution to solving problems in various fields. This article proves the effectiveness of an optimization model based on the metaheuristic method for the analysis of hydraulic parameters of drinking water distribution pipes. The metaheuristic methods explored are Differential Evolution (DE) algorithm, Particle Swam Optimization (PSO) algorithm and CODEQ algorithm. The effectiveness of the three methods is measured relative by comparing the results of the analysis of the three models with the results from Newton Raphson method and Monte Carlo simulation method. The analysis shows that the optimization model based on the DE, PSO and CODEQ algorithms is very effective for solving problems on a simple network that has 6 pipe elements and 5 service nodes. The results obtained have a level of accuracy as good as Newton Raphson method. In the case of complex networks that have 32 pipe elements and 21 service nodes, there is an indication of performance degradation which is indicated by a decrease in fitness value. In this case, Newton Raphson method still shows its consistency. The optimization model based on the metaheuristic method is still far more effective than the Monte Carlo simulation method, although it is not as effective as Newton Raphson method. The Monte Carlo simulation method is not recommended for hydraulic pipe network analysis, even for simple networks.

Software Testing for cyber Security

Software testing plays a vital role in software security because hackers attack on a system through back channels which they can easily find if there is any error or bug exists in the software. The software security failure can cause the unbearable loss for IT companies and other organizations. Cyber security is another big issue for computer users' personal data as all their information is vulnerable because of easy excess, visibility and availability. Therefore, software testing is also useful to secure the personal information. In this article, cyber security testing based on particle swam optimization algorithm (CST) is proposed for testing of software cyber security testing. CSTPSOA is a PSO base technique which is used to solve the complex multi-level problems and is also used for optimization. In the CST method PSO is used for the optimization of test cases for cyber security testing.

Optimized Low Complexity Service Based onReliable Routing Algorithm using PSO for Vehicular Ad hoc Networks (VANETs)

Objectives: To explored the highlighted obliged QoS routing issue in Vehicular Ad hoc Networks (VANETs). Methods/ Statistical Analysis: The issues are tended to firstly by working up an association faithful quality model considering the topological and numerical properties of vehicular advancements and speeds. The VANET correspondence diagram in advancing chart hypothesis is used to model and organize the formed association trustworthiness shown into it. In light of the consequent intensified chart diagram illustrate, the most tried and true course in the framework is picked. However course assurance does not ensure awesome transmission as well. Findings: Vehicular extraordinarily designated Systems (VANETs) are a particular sort of remote framework made by vehicles giving among themselves and with roadside base stations. A broad assortment of organizations has been made for VANETs going from security to infotainment applications. A key need for such organizations is that they are offered with Nature of Administration Quality of Service (QoS) guarantees similar to organization unfaltering quality and availability. QoS routing expect an essential part in recognizing courses that meet the QoS necessities of the offered organization over VANETs. For filtering the achievable courses it subject to various QoS goals is with everything taken into account a NP-difficult issue. Furthermore, the reliability of the routing ought to be given unprecedented thought as correspondence associations, as frequently as conceivable in VANETs. In present situation, the most existing QoS routing calculations are been PSO algorithm for stable VANET system without considering the security and wellbeing of the routing procedure. Along these lines, they are not appropriate for applications in VANETs. Application/Improvements: Low complexity nature advanced administration dependable routing calculation is made to ensure to lessen transmission redundancy with use of Subterranean insect State estimation with better eventual outcomes of course decision and transmission considering better request and sorting calculations in the Particle Swam Optimization (PSO) algorithm work calculation for extremely powerful VANET system with less preferred quality in evaluation with favoured data transmission over interchange QoS counts as shown by the results and satisfy QoS requirements of specific traffic using PSO in the VANET routing protocol environments. Meanwhile, an optimized function with PSO is also proposed to make scheduling decisions so that the dropped packet rates are reduced. The results of PSO algorithm simulations are better and feasible as compared to the previous techniques.

PSO-Enabled Privacy Preservation of Data Clustering

Background/Objective: Privacy is the vital issue when sharing of the data comes into picture. The demand and scope for privacy is increasing day-by-day as data storage techniques have emerged from standalone database to distributed database and then progressed to parallel databases. K-means and Fuzzy C-means (FCM) are the frequently used clustering algorithms for standalone database, distributed database and parallel databases. The current paper highlights Particle Swam Optimization algorithm along with Fuzzy C-means clustering algorithm technique for preserving the privacy on distributed databases. Methods/Statistics Analysis: The experimentation is performed by means of the datasets accessible in the UCI machine-learning repository. The main benefit of the suggested technique is that, this technique will assess in terms of their privacy of cluster. Therefore, the technique plans to give improved visibility for the protected data. The technique is executed in the working platform of MATLAB and the effects will be examined to show the presentation of the suggested clustering technique. Findings: The performance of the proposed clustering technique based on privacy preserving is analyzed for accuracy and Database Different Ratio (DBDR) on six UCI medical related data sets namely Hugerian dataset, Cleveland data set, Reprocessed Hugerian data sets, Long Beach V.A data, BUPA and liver disorder data. Performance improvement observed in the range of 3%-6% on each of the six data sets compared to K-means algorithm. Application/Implementation: The main benefit of the suggested technique is that technique will have to assess in terms of their privacy of cluster. Therefore, the technique plans to give improved visibility for the protected data

이동형 재밍환경에서 널 합성을 위한 적응형 널링 알고리즘

본 논문에서는 단일포트시스템으로 설계된 배열안테나에서 재밍신호나 간섭신호 방향으로 방사패턴에 널(null)을 형성하는 적응 알고리즘을 제안한다. 제안한 적응 알고리즘은 재밍신호나 간섭신호의 크기나 입사방향에 대한 사전정보를 요구하지 않으며, 배열안테나의 일부 RF 경로의 위상을 제어하는 부분적응(partially adaptive) 알고리즘이다. 제안한 적응 알고리즘은 최적화 알고리즘의 한 종류인 PSO(Particle Swam Optimization) 알고리즘과 gradient-based 섭동 적응 알고리즘을 혼합하여 시간에 따라 간섭신호의 입사방향이 변화하는 이동형 재밍환경에서도 적응적으로 안정적인 널링 성능을 가진다. In this paper, an adaptive nulling algorithm which can be used to form nulls in the direction of jammer or interference signals in array antennas of single port system is proposed. The proposed adaptive algorithm does not require a priori knowledge of the incoming signal direction and can be applied to the partially adaptive arrays. This algorithm is the combination of the PSO(Particle Swam Optimization) algorithm and the gradient-based perturbation adaptive algorithm, which shows stable nulling performance adaptively even on the moving jammer environment where the incident direction of the interference signal is changing with time.

Optimization Methods used for Automatic Image Annotation/Retrieval: A Survey

Automatic Image annotation is a dominant research area in computer science. It is concerned with the storage of images and assigning meaningful keywords to it. There are several methods developed for efficient automatic image annotation which uses various optimization techniques. The purpose of this paper is to show the survey study done on the optimization techniques for Image annotation. An image has several preeminent characteristics like colour texture, shape etc. These different descriptors of the images can form a combined feature vector. Optimization algorithms such as Particle swam optimization algorithm, Genetic algorithm etc can be used for optimum feature selection.

Unified particle swarm optimization with random ternary variables and its application to antenna array synthesis

Antenna array synthesis sometimes involves both real and integer parameters as a mixed integer optimization problem. In this paper, a modified particle swam optimization (PSO) algorithm is proposed in order to deal with real and integer variables in a unified manner. Two major modifications are made compared to the classical PSO algorithm. First a unified vector having continuous values between 0 and 1 is defined, and at each iteration this vector (or part of it) is mapped to real variables and/or rounded to integer variables, which makes the updating process unified for any type of parameters. Second, random ternary variables are introduced to compensate quantization errors caused by the rounding-off operations, which could accelerate the speed of convergence and lead to improved topology exploration capability. In order to demonstrate the effectiveness of the proposed method, three previous examples about antenna array synthesis are revisited, and better results than those in the existing literatures are obtained in all these examples.

Combined particle swarm optimization and linear discriminant analysis for landslide image classification: application to a case study in Taiwan

In Taiwan many reservoirs are constructed in mountain areas. Unfortunately, several earthquakes shook the soil, and typhoons brought a huge amount of water to the reservoir zone. In the past studies, remote-sensing image data were used to effectively monitor the landslide near reservoirs. In recent years, linear discriminant analysis (LDA) has become a well-known method for image classification. However, there are few studies to optimize the linear classification function. While the ancillary information has been adopted easily by new methodologies, the ancillary information must be examined by a landslide image classification system. To explore the effects of optimization on the LDA equations, three approaches were compared: (a) conventional LDA; (b) combined discrete rough sets and LDA (DRS + LDA), which identify the core factors and the corresponding thresholds of landslide occurrence; and (c) combined particle swam optimization algorithm and LDA (PSO + LDA), which optimizes the parameters of LDA equation to attain the best classification outcomes. The above methods were applied to a reservoir region in Taiwan, and the following classification results were obtained. The application of DRS + LDA in our case study reduced the number of ancillary attributes from 14 to 5, and resulted in an accuracy rate of 0.83. On the other hand, the application of PSO + LDA resulted in the same accuracy rate as that of DRS + LDA, whereas the accuracy rate of conventional LDA was found to be 0.78.

PI Controller Design for DFIG Wind Power Generation System RSC Based on PSO Algorithm

PID controller has widely used in control system for its simple structure, good control effect and strong robustness, the PI control strategy was introduced into rotor side converter of DFIG control with the mathematical models and the structure of stator flux-oriented vector control. Particle swam optimization algorithm was a new random optimization technology which has the features of rapid calculation speed. The PI controller design for RSC current loop and speed loop control was analyzed, and the PSO algorithm was presented for PI controller design, the simulation experiments demonstrated that the algorithm was suitable for the RSC control system with PSO remarkable search capability.

Performance Prediction of Differential Fibers with a Bi-Directional Optimization Approach.

This paper develops a bi-directional prediction approach to predict the production parameters and performance of differential fibers based on neural networks and a multi-objective evolutionary algorithm. The proposed method does not require accurate description and calculation for the multiple processes, different modes and complex conditions of fiber production. The bi-directional prediction approach includes the forward prediction and backward reasoning. Particle swam optimization algorithms with K-means algorithm are used to minimize the prediction error of the forward prediction results. Based on the forward prediction, backward reasoning uses the multi-objective evolutionary algorithm to find the reasoning results. Experiments with polyester filament parameters of differential production conditions indicate that the proposed approach obtains good prediction results. The results can be used to optimize fiber production and to design differential fibers. This study also has important value and widespread application prospects regarding the spinning of differential fiber optimization.