Application Of Particle Swarm Optimization Algorithm Research Articles

Fuel consumption estimation for jet aircraft: a mathematical model development

PurposeReducing aircraft fuel consumption has become a paramount research area, focusing on optimizing operational parameters like speed and altitude during the cruise phase. However, the existing methods for fuel reduction often rely on complex experimental calculations and data extraction from embedded systems, making practical implementation challenging. To address this, this study aims to devise a simple and accessible approach using available information.Design/methodology/approachIn this paper, a novel analytic method to estimate and optimize fuel consumption for aircraft equipped with jet engines is proposed, with a particular emphasis on speed and altitude parameters. The dynamic variations in weight caused by fuel consumption during flight are also accounted for. The derived fuel consumption equation was rigorously validated by applying it to the Boeing 737–700 and comparing the results against the fuel consumption reference tables provided in the Boeing manual. Remarkably, the equation yielded closely aligned outcomes across various altitudes studied. In the second part of this paper, a pioneering approach is introduced by leveraging the particle swarm optimization algorithm (PSO). This novel application of PSO allows us to explore the equation’s potential in finding the optimal altitude and speed for an actual flight from Algiers to Brussels.FindingsThe results demonstrate that using the main findings of this study, including the innovative equation and the application of PSO, significantly simplifies and expedites the process of determining the ideal parameters, showcasing the practical applicability of the approach.Research limitations/implicationsThe suggested methodology stands out for its simplicity and practicality, particularly when compared to alternative approaches, owing to the ready availability of data for utilization. Nevertheless, its applicability is limited in scenarios where zero wind effects are a prevailing factor.Originality/valueThe research opens up new possibilities for fuel-efficient aviation, with a particular focus on the development of a unique fuel consumption equation and the pioneering use of the PSO algorithm for optimizing flight parameters. This study’s accessible approach can pave the way for more environmentally conscious and economical flight operations.

Optimization application of particle swarm algorithm in civil engineering

In the construction and development of modern society, civil engineering as the basis of promoting the steady development of the industry economy and ensuring the stability of urban residents' lives, with the continuous innovation of social economy and science and technology, is now facing severe challenges and rich opportunities. Particle swarm optimization (PSO), as a simulation of bird predation process, can obtain the optimal solution of searching space, and its application in civil engineering structure optimization and damage detection can help workers find the existing problems faster, and put forward effective solutions. Therefore, on the basis of understanding the development status of modern civil engineering technology, according to particle swarm optimization algorithm and improved scheme, this paper deeply discusses how to apply particle swarm optimization algorithm in the damage detection of civil engineering structures. The final experimental results show that the improved particle swarm optimization algorithm has unique application advantages and plays an important role in civil engineering.

Privacy Protection and Intrusion Detection System of Wireless Sensor Network Based on Artificial Neural Network.

With the increasing openness and development of network technology, the network based on the wireless sensor network system has increasingly become an important tool for human social life and production, but it also brings some network security problems. Among them, focus is on network privacy disclosure and foreign intrusion and the research of intrusion detection and privacy protection has increasingly become an important topic of network security. This paper deeply studies the wireless sensor network system based on neural network on the basis of traditional privacy protection and intrusion detection system. Firstly, it applies particle swarm optimization algorithm and constructs a wireless sensor network intrusion detection system based on particle swarm optimization algorithm. The system includes important modules such as data extraction, data analysis, data feedback, and auxiliary decision-making. Compared with other algorithms, particle swarm optimization algorithm does not rely on problem information. It mainly uses real numbers to solve, so the algorithm has strong universality. At the same time, its corresponding principle is simple and easy to implement and less parameters need to be adjusted. Compared with other algorithms, particle swarm optimization algorithm has fast convergence speed and little memory requirement for the computer. At the same time, this paper uses the leap of particle swarm optimization algorithm to make it easier to find the global optimal solution. At the corresponding level of wireless sensor network privacy protection, based on the original data aggregation privacy protection scheme, this paper proposes a privacy protection scheme based on polynomial regression and a user privacy protection scheme based on the same state encryption, which further improves the security of privacy protection and facilitates the management of information. To realize the integrity of user privacy information protection, this paper realizes the decryption of data based on the correlation between binary metadata and compares the corresponding decrypted data with aggregated data, so as to complete the integrity of privacy data protection. The experimental results show that the binary metadata correlation decryption method proposed in this paper and the introduction of the corresponding particle swarm optimization algorithm improve the stability of the system by about 10%, the corresponding system security by a positive proportion, and the integrity of private data by about 10%; therefore, the algorithm proposed in this paper has obvious advantages.

The Application of Art Therapy Based on Particle Swarm Optimization Method for Preschool Children’s Mental Health

Art provides a starting point for children to understand works of art. In the initial stage of life, the development of vision and movement is particularly important, and painting helps coordinate the work of these systems. Art dispels the mystery of art and helps children connect with art classics, thus understanding the language and structure of art. In particle swarm optimization, the inertia weight is constant, but with the increase of iteration times, the inertia weight may become larger or smaller, so it tends to search globally or locally. This paper first analyzes what art therapy is from the basic concepts of art and art education and the stage characteristics of children’s painting and then studies the application of art therapy based on particle swarm optimization in preschool children’s art teaching. Finding the global optimal fitness function in the research process not only simplifies the computational complexity but also approximates the maximum coverage effect provided by the current network. The results show that applying particle swarm optimization algorithm to the research of mental health treatment of preschool children can establish an effective evaluation program in the practical research of children’s art treatment.

Optimization of retrial queue with unreliable servers subject to imperfect coverage and reboot delay

ABSTRACT A retrial queue with constant retrial policy, unreliable servers and the failed servers may not be detected is investigated in this paper. Upon arrival, a customer either is attended to one of the available servers or enters a retrial orbit and retries in a random time as all servers are not available. The unreliable server can be breakdown when he is working. The breakdown server may not be detected due to some fault matters. Under such a situation, the repair facility will issue a reboot operation to locate the non-detected breakdown server. Once the breakdown server is detected and located, he will be sent for repair. After utilizing the general theory of matrix-geometric approach to determine the stationary distribution, some performance measures are proposed. A single-objective model aiming at minimizing the average cost is constructed. We apply particle swarm optimization algorithm and genetic algorithm to find the optimum combination of parameters. We also formulate a bi-objective model aiming at minimizing the expect cost and the mean waiting time of customers in orbit. Two multi-objective algorithms are developed to solve it.

A PSO based multi-domain virtual network embedding approach

This paper proposed a multi-domain virtual network embedding algorithm based on multi-controller SDN architecture. The local controller first selects candidate substrate nodes for each virtual node in the domain. Then the global controller abstracts substrate network topology based on the candidate nodes and boundary nodes of each domain, and applies Particle Swarm Optimization Algorithm on it to divide virtual network requests. Each local controller then embeds the virtual nodes of the divided single-domain virtual network requests in the domain, and cooperates with other local controllers to embed the inter-domain virtual links. Simulation experimental results show that the proposed algorithm has good performance in reducing embedding cost with good stability and scalability.

An intelligent method to design laser resonator with particle swarm optimization algorithm

In order to design a complex laser resonator with multi-parameters, the method of particle swarm optimization (PSO) algorithm is employed. The parameters influencing the resonator stability and mode size distribution are taken into consideration, and the stability criteria index and the mode size distribution are used as target values. The absolute values of the differences between practical and the target values are set as the fitness function for the PSO. By minimizing the fitness function, a laser resonator with the optimized cavity parameters can be found. The analyses for the design example demonstrate the feasibility and validity of the PSO method in the computer aided design of multi- parameters laser resonator. Applying PSO algorithm in the intelligent design of solid state laser resonators can realize the transition from manual trial-and-error to computer intelligent design of the laser resonators.

An Analysis of Breast Cancer DNA Sequences Using Particle Swam Optimization

Conceptual Breast tumour conclusion, examination, and visualization are essential research challenges in Bioinformatics. Bosom tumour analysis incorporates recognizing of malignancy bumps and ordinary tissue. Investigation incorporates the present phase of the malignancy tissue and anticipation incorporates expectation of repeat of the bosom tumour in future ages in light of structure and game plan of the individual DNA succession. This paper investigations bosom disease DNA succession to anticipate event of bosom tumour utilizing Particle Swarm Optimization (PSO).PSO procedure is a populace based pursuit calculation that mirrors the social conduct of swam. As the piece of investigation of bosom disease in human, the DNA arrangements of ordinary bosom tissue are contrasted and DNA groupings of bosom tumour tissue utilizing PSO... The distinction between the ordinary and breast cancer disease DNA sequences are broke down in view of the summarized values generated by applying PSO algorithm. 

Apply Particle Swarm Optimization Algorithm to Measure the Software Quality

The process of improvement software quality began from early stages of software engineering development. It uses multiple quality metrics which are very important in software development. To calculate the standards quality of in software testing has been adopted. The software testing is focusing on the Software defect. In this paper is proposed new methods which combine the particle swarm optimization (PSO) to handle the best features Extraction with back-propagation networks to testing and evaluation of the data set . The paper depended database for NASA standards data. The result and experiment method improved quality performance for all classification methods used in the research"Combining Particle Swarm Optimization based Feature Selection and Bagging for Software Defect Prediction ".

An Optimized Fuzzy-Padé Controller Applied to Attitude Stabilization of a Quadrotor

In this research, an Optimized Fuzzy-Pade Controller (OFPC) for attitude stabilization of a quadrotor is proposed by using Pade approximants and fuzzy singleton rules. To determine unknown coefficients of the Pade approximant, a PD-type Fuzzy Logic Controller (FLC) is first designed. As the number of the fuzzy singleton rules extracted from the FLC is less than the number of the unknown coefficients, some of them cannot be determined using the rules. The coefficients not specified are determined applying particle swarm optimization algorithm to increase convergence speed and decrease energy consumption. The simulation results for the OFPC demonstrate faster convergence speed, lower power consumption, larger convergence region, better robustness, and faster computational time in comparison to the FLC. Unlike the FLC, the OFPC is fast enough to be implemented on the microcontroller of the quadrotor. The experimental results indicate that the proposed controller quickly stabilizes the quadrotor, even with externally applied disturbances. The proposed approach possesses certain advantages over FLCs, and it can be employed wherever FLCs are not applicable due to the high computational burden.

Satellite Image Segmentation with the Application of PSO Algorithm

Satellite imaging consists of capturing images of the Earth through a series of artificial satellites. These images contain an abundance of information that can be used in several applications such as fishing, agriculture, regional planning, biodiversity conservation and many others. Digital image processing can help overcome the limitations of human vision by extracting key information from these images at a much higher rate through the speed of automation. This paper aims to achieve that by exploiting the potential of the Particle Swarm Optimization (PSO) algorithm in image segmentation. Various satellite images were segmented using PSO algorithm before a trace of the objects that have been isolated in the image was run to evaluate the accuracy of segmentation. Three objective measurements which are Peak Signal to Noise Ratio (PSNR), Root Mean Square Error (RMSE), and Mean Absolute Error (MAE), were made on the outputs of the segmentation using PSO algorithm and the traditional segmentation technique which is Otsu’s method for comparison. The proposed method which applies the PSO algorithm proved to be superior in producing images of higher quality and accuracy as compared to the traditional segmentation technique.

Beamforming of a Linear Array Applying PSO Algorithm with Restrictive Approach

This paper presents a four-element linear arraycomposed of E-shaped microstrip antennas designed to switched-beam application in ISM band. Particle Swarm Optimization(PSO) algorithm is applied to optimize four different sets ofamplitude and progressive phase shift to achieve four distinctradiation patterns controlling the major lobe direction andsidelobe level. For this application, two restrictive approachesare presented for the implementation of PSO algorithm in orderto improve the algorithm convergence to feasible solutions.

On PSO Based BP Neural Network

Particle swarm optimization (PSO) based BP neural network is introduced , which is superior to the traditional BP neural network . The traditional BP neural network and PSO algorithm is illustrated respectively, and introduces how to apply PSO algorithm in BP neural network. The numerical simulation proves that PSO-BP neural network performs better than traditional BP neural network.

Applying particle swarm optimization algorithm to roundness error evaluation based on minimum zone circle

Minimum zone circle (MZC) method and least square circle (LSC) method are two most commonly used methods to evaluate roundness, but only the MZC method complies with the standard definition and can obtain the minimum roundness error value. The determination of the center of MZC is a nonlinear optimization problem which is suitable to be solved by particle swarm optimization (PSO) algorithms. In this paper, the standard PSO algorithm was introduced and theory analysis about the impact of value selection of some important parameters, such as inertia weight ω, on the algorithm’s stability and convergence was carried on so as to provide basis for giving these parameters better values. Furthermore, the superiority of making ω decrease linearly with iterations was verified through a computation experiment in terms of stability and accuracy, compared with the other three cases of ω=1, 0.5, 0. Based on the analysis, the novel PSO algorithm, with ω decreasing linearly from 0.9 to 0.4 and the LSC center as the initial positions of the particles, is implemented to obtain MZC-based roundness errors of sampling points collected from circular section profiles by a coordinate measuring machine (CMM). By comparing the novel PSO–MZC results with the LSC-based results, it is concluded that the former are a little smaller than the latter, which verifies that the novel PSO algorithm is feasible to calculate roundness error and the fact that a LSC-based one is generally larger than a MZC-based result; the values of the two roundness errors are both related to sample size and increase with an increase in the sample size with a decreasing increment.

Improved Extraction Algorithm of Outside Dividing Lines in Watershed Segmentation Based on PSO Algorithm for Froth Image of Coal Flotation

It is difficult to exact accurate bubble size and to make image recognition more reliable for forth image of coal floatation because of low contrast and blurry edges in froth image. An improved method of getting outside dividing lines in watershed segmentation was proposed. In binarization image processing, threshold was optimized applying particle swarm optimization algorithm(PSO)combining with 2-D maximum entropy based gray level co-occurrence matrix. After distance transform, outside dividing lines have been exacted by watershed segmentation. By comparison with Otsu method, the segmentation results have shown that the gotten external watershed markers are relatively accurate, reasonable. More importantly, under segmentation and over segmentation were avoided using the improved method. So it can be proved that extraction algorithm of outside dividing lines based on PSO is effective in image segmentation

Research on Information Applied Technology with Swarm Intelligence for the TSP Problem

As a swarm intelligence algorithm, particle swarm optimization (PSO) has received increasing attention and wide applications in information applied technology. This paper investigates the application of PSO algorithm to the traveling salesman problem (TSP) on applied technology. Proposing the concepts of swap operator and swap sequence, we present a discrete PSO algorithm by redefinition of the equation for the particles velocity. A computational experiment is reported. The results show that the method proposed in this paper can achieve good results.

Modified Binary Particle Swarm optimization algorithm application to real-time task assignment in heterogeneous multiprocessor

Task assignment in a heterogeneous multiprocessor is a NP-hard problem, so approximate methods are used to solve the problem. In this paper the Modified Binary Particle Swarm Optimization (Modified BPSO) algorithm and Novel Binary Particle Swarm (Novel BPSO) Optimization are applied to solve the real-time task assignment in heterogeneous multiprocessor. The problem consists of a set of independent periodic task, which has to be assigned to a heterogeneous multiprocessor without exceeding the utilization bound. The objective is to schedule maximum number of tasks with minimum energy consumption. The execution times and deadlines of the tasks are assumed to be known. Here Modified BPSO performance is compared with Novel BPSO and Ant Colony Optimization algorithm (ACO). Experimental results show that Modified BPSO performs better than Novel BPSO and ACO for consistent utilization matrix and ACO performs better than Modified BPSO and Novel BPSO for inconsistent utilization matrix.

Optimization of ICDs' Port Sizes in Smart Wells Using Particle Swarm Optimization (PSO) Algorithm through Neural Network Modeling

Oil production optimization is one of the main targets of reservoir management. Smart well technology gives the ability of real time oil production optimization. Although this technology has many advantages; optimum adjustment or sizing of corresponding valves is still an issue to be solved. In this research, optimum port sizing of inflow control devices (ICDs) which are passive control valves is focused on by designing a neural network to simulate reservoir behavior and applying Particle Swarm Optimization algorithm to find optimum port size for ICDs. Indeed; this work eliminates the need for lots of expensive and time consuming iterations through reservoir simulator. The objective of the work is to maximize the oil production.

Application of particle swarm optimization and perceptual map to tourist market segmentation

This study intends to apply particle swarm optimization algorithm to cluster analysis for investigating market segmentation of Taiwanese tourists based on their motivation to visit Indonesia. In addition, Taiwanese tourists’ preference of several types of tourism destinations offered in Indonesia will also be studied. According to the cluster analysis results, we will propose marketing strategy based on market segmentation and tourists’ preferences revealed. The computational results reveal that there were four clusters formed. Moreover, by using perceptual map, it was also known that most of respondents tend to visit heritage, culture, and nature-based tourism destinations offered in Indonesia.

Improved Particle Swarm Optimization Algorithm Application of the Variable Batch Flow-Shop Problem

Improved Particle Swarm Optimization Algorithm Application of the Variable Batch Flow-Shop Problem