Particle Swarm Method Research Articles

In-Plane Flexible Ring Tire Model—Part 1: Modeling and Parameter Identification

ABSTRACT The tire model is essential for accurate and efficient vehicle dynamic simulation. In this article, an in-plane flexible ring tire model is proposed, in which the tire is composed of a rigid rim, a number of discretized lumped mass belt points, and numerous massless tread blocks attached on the belt. One set of tire model parameters is identified by approaching the predicted results with ADAMS® FTire virtual test results for one particular cleat test through the particle swarm method using MATLAB®. Based on the identified parameters, the tire model is further validated by comparing the predicted results with FTire for the static load-deflection tests and other cleat tests. Finally, several important aspects regarding the proposed model are discussed.

Ultimate Load Capacity of Optimal Designed Angelina™ Beams

This study briefly summarizes the results of experimental tests performed on optimal designed Angelina™ steel beams. The objective of the investigation was to study the effect of hole geometry on the mode of failure and ultimate strength of such beams under loading conditions. For this purpose, six optimal designed Angelina™ beams are tested in a self-reacting frame to determine the ultimate load carrying capacities of these new generation web-expanded beams. The specimens were all fabricated from IPN beams and were expanded to almost 1.5 times the original depth. All specimens were fabricated from ASTM A-36 steel. The design methods for the specimens will be the harmony search algorithm and particle swarm method which are stochastic search techniques. The minimum weight is taken as the design objective while the design constraints are implemented from the SCI (Steel Construction Institute). Design constraints include the displacement limitations, overall beam flexural capacity, beam shear capacity, overall beam buckling strength, web post flexure and buckling, vierendeel bending of upper and lower tees and local buckling of compression flange. The design methods adopted in this publication are consistent with BS5950.

APPLICATION PARTICLE SWARM ALGORITHM TO MINIMIZE THE COST OF CONDUCTING MULTIVARIATE EXPERIMENT

Context. The actual problem of obtaining a sequence of experiments in the conduct of a full factor experiment ensuring its minimum cost has been solved. Objective – is to create a method for optimizing multifactor experimental plans using an optimization algorithm for the particle swarm. Method. A method is proposed for constructing an optimal experiment design matrix for the cost of implementation using the particle swarm algorithm. The particle swarm method is based on modeling the behavior of the particle population in the parameter space of the optimization problem. In the beginning, the number of factors and the cost of the transition for each level of factors are introduced. Then, taking into account the input data, a composite matrix of experiment planning is formed. The particles are scattered randomly across the entire composite experiment design matrix and each particle has a random velocity vector. After that, the particles begin to move along the rows and columns of the matrix. At each point where the particle visited, the value of the experiment is calculated. In this case, each particle remembers which (and where) the best value of the cost of the experiment, she personally found and where the point is located, which is the best among all the points that explored the particles. At each iteration, the particles correct their velocity (module and direction) in order to be closer to the best point on the one hand, which she found herself and, at the same time, to approach the point that is currently globally better. After a certain number of iterations, the particles are collected near the best point. Then the current coordinate of each particle is corrected. After this, the cost of the experiment is calculated at each new point, each particle checks whether the new coordinate has become the best among all the points where it visited. Then, among all the new points, we check whether we have found a new globally better point, and if found, remember its coordinates and the value of the cost of conducting the experiment in it. Then the gain is calculated in comparison with the initial cost of the experiment. Results. The software that implements the proposed method is developed, which was used in carrying out computational experiments to study the properties of the method. Conclusions. The conducted experiments confirmed the efficiency of the proposed method and the software that implements it, and also allow them to be recommended for application in practice when constructing optimal experimental design matrices.

Modeling and Optimization of the Magnetohydrodynamic Conduction Pump by Particle Swarm Method

The paper is dedicated to the study and the optimization of a magnetohydrodynamic conduction pump. Electromagnetic pumps have several advantages to mechanical pumps, they do not use a moving mechanical part, unlike traditional electric motors, and they transform electromagnetic energy directly into kinetic energy. A fluid is set in motion in a magnetic field by an electric field supplying an electric current to the terminals of electrodes immersed in the fluid. The optimization procedure based on the particle swarm method uses a fitness function as the minimum of the mass. The electromagnetic model is carried out by the finite volume method and the steady fluid flow by COMSOL software. The optimized results of the performance characteristics of the electromagnetic pump are obtained.

Time-Optimal Control of a Simple Pendulum with a Movable Pivot. Part 1

A modification of control constraints in optimal control problems for a simple pendulum with a movable pivot is substantiated. The problem of time optimal control of the motion of a simple pendulum for the classical and modified constraints is formulated. By analytically integrating the system of equations of motion, the optimization problem is reduced to a nonlinear programming problem with constraints. This problem is solved numerically with the particle swarm method. The proposed method of solving time optimal control problems is generalized to mathematical models that can be integrated analytically.

LUCID PARTICLE SWARM OPTIMIZATION ALGORITHM FOR SOLVING OPTIMAL REACTIVE POWER PROBLEM

This paper presents, Lucid Particle Swarm Optimization (LPSO) algorithm for Solving Optimal Reactive Power Problem. Particle swarm method is typically made up of a population of simple agents intermingle locally with one another and with their surroundings, with the aim of locating the optima within the operational environment. In this paper, a robust and Lucid particle swarm optimization framework based on multi-agent system is presented, where learning capabilities are integrated into the particle agents to dynamically fiddle with their optimality behaviours. Self-Sufficiency is achieved by the use of communicators that separate an agent’s individual operation from that of the swarm, thereby making the system more robust. The proposed Lucid Particle Swarm Optimization (LPSO) algorithm has been tested on standard IEEE 118 & practical 191 bus test systems and simulation results show clearly about the premium performance of the proposed algorithm in reducing the real power loss.

Ultimate Load Capacity of Optimal Designed Angelina™ Beams

This study briefly summarizes the results of experimental tests performed on optimal designed Angelina™ steel beams. The objective of the investigation was to study the effect of hole geometry on the mode of failure and ultimate strength of such beams under loading conditions. For this purpose, six optimal designed Angelina™ beams are tested in a self-reacting frame to determine the ultimate load carrying capacities of these new generation web-expanded beams. The specimens were all fabricated from IPN beams and were expanded to almost 1.5 times the original depth. All specimens were fabricated from ASTM A-36 steel. The design methods for the specimens will be the harmony search algorithm and particle swarm method which are stochastic search techniques. The minimum weight is taken as the design objective while the design constraints are implemented from the SCI (Steel Construction Institute). Design constraints include the displacement limitations, overall beam flexural capacity, beam shear capacity, overall beam buckling strength, web post flexure and buckling, vierendeel bending of upper and lower tees and local buckling of compression flange. The design methods adopted in this publication are consistent with BS5950.

Experimental test of an online ion-optics optimizer

A technique has been developed and tested to automatically adjust multiple electrostatic or magnetic multipoles on an ion optical beam line – according to a defined optimization algorithm – until an optimal tune is found. This approach simplifies the process of determining high-performance optical tunes, satisfying a given set of optical properties, for an ion optical system. The optimization approach is based on the particle swarm method and is entirely model independent, thus the success of the optimization does not depend on the accuracy of an extant ion optical model of the system to be optimized. Initial test runs of a first order optimization of a low-energy (<60 keV) all-electrostatic beamline at the NSCL show reliable convergence of nine quadrupole degrees of freedom to well-performing tunes within a reasonable number of trial solutions, roughly 500, with full beam optimization run times of roughly two hours. Improved tunes were found both for quasi-local optimizations and for quasi-global optimizations, indicating a good ability of the optimizer to find a solution with or without a well defined set of initial multipole settings.

Анализ качества электроэнергии в распределительной сети Египта и выбор устройств компенсации реактивной мощности

The article presents the results from measuring and analyzing the parameters characterizing the quality of electric power in the distribution network of Middle Egypt. This study is part of the research project for further development of the Egyptian electric network. Problems concerned with deviations from the permissible level of voltage in the distribution network are analyzed. Measures to normalize the voltage levels by using reactive power compensation devices are proposed. A new hybrid algorithm based on the particle swarm method was applied for arranging the compensating devices. The network operating conditions were simulated in the Matlab software system environment. The proposed approach for arranging the compensating devices based on optimization using the hybrid particle swarm method has shown high efficiency for normalizing the voltage levels and reducing active power losses.

Distribution of resources based on hybrid models of swarm intelligence

The composite architecture of the multi-agent bionic search system is proposed to solve the general distribution problem based on swarm intelligence and genetic evolution. Three approaches to the construction of such architecture are considered. The connecting link of this approach is a unified data structure that describes the solution of the problem in the form of a chromosome. The new principles and methods of coding and decoding of chromosomes for the representation of the general distribution problem considered in this paper exclude incorrect solutions, are distinguished by simplicity and linear estimates of temporal and spatial complexity. A modified paradigm of the particle swarm method is proposed. To organize the swarm movement of particles in hyperspace of solutions, a directed mutation operator has been developed. Experiments have shown that the quality of the solutions in the hybrid algorithm is 10 to 15% better than the genetic and swarm algorithms. The overall estimate of time complexity for any hybridization approach does not exceed the estimate of the time complexity of the genetic algorithm and lies within the range О(n2)–О(n3).

Feed-Forward Neural Network-Based Predictive Image Coding for Medical Image Compression

The generation of high volume of medical images in recent years has increased the demand for more efficient compression methods to cope up with the storage and transmission problems. In the case of medical images, it is important to ensure that the compression process does not affect the image quality adversely. In this paper, a predictive image coding method is proposed which preserves the quality of the medical image in the diagnostically important region (DIR) even after compression. In this method, the image is initially segmented into two portions, namely DIR and non-DIR portions, using a graph-based segmentation procedure. The prediction process is implemented using two identical feed-forward neural networks (FF-NNs) at the compression and decompression stages. Gravitational search and particle swarm algorithms are used for training the FF-NNs. Prediction is performed in both a lossless (LLP) and near-lossless (NLLP) manner for evaluating the performances of the two FF-NN training algorithms. The prediction error sequence which is the difference between the actual and predicted pixel values is further compressed using a Markov model-based arithmetic coding. The proposed method is tested using CLEF med 2009 database. The experimental results demonstrate that the proposed method is equipped for compressing the medical images with minimum degradation in the image quality. It is found that the gravitational search method achieves higher PSNR values compared to the particle swarm and backpropagation methods.

ПРИМЕНЕНИЕ МЕТОДА РОЯ ЧАСТИЦ ДЛЯ ОПТИМИЗАЦИИ КОМПОЗИЦИОННЫХ ПЛАНОВ ВТОРОГО ПОРЯДКА

Develop methods and software for the optimization of composite second order plans using the method of particle swarm. Composite second-order plans for use in the construction of quadratic mathematical models of the objects and systems. Analysis of the known methods of synthesis of optimal cost and time plans composite second order. Efficiency of the algorithm was tested in the study of the thermal treatment process of the porous material and the regimes of the technical system maintenance. Demonstrated its effectiveness in comparison with the following methods: successive approximation, branch and bound and taboo search

Integrated Scheduling of Ladder layout design in Flexible Manufacturing System using Non-Traditional optimization techniques

The Ladder layout design (LLD) problem generally crop ups when a manufacturing cluster intended to enlarge their making capacity and/or decrease the transportation cost or material flow through a predefined sequence of machines for manufacturing a product. The problem is known to be Non-deterministic Polynomial (NP) hard, which is usually solved by metaheuristics. In this paper, multi-objective optimization is considered related to FMS scheduling which acts as a constraint in configuring the ladder layout in optimum manner by various algorithms such as Metaheuristics like PSM, SA etc. The various Ladder layout problems are tested for performance of objective function with respect to computational time and number of iterations involved in SA and PSM. A necessary code is generated in C++ and the code is run by the IDE tool. The results of the different optimization algorithms such as simulated annealing (SA), Particle swarm method (PSM) are compared. Finally, it is observed that most of solutions obtained from SA are marginally better than PSM except in some exceptional cases but the computational time taken by SA were almost same as PSM.

Adaptive modification of the particle swarm method based on dynamic correction of the trajectory of movement of individuals in the population

Yu.V. Minaeva - Senior Lecturer, Department of Computer Aided Design Systems and Information Systems, Voronezh State Technical UniversityAddress: 14, Moscow Avenue, Voronezh, 394026, Russian FederationE-mail: julia_min@mail.ru Evolutionary search methods are successfully used for deferent modeling and optimization tasks due to their universality and the relative simplicity of realization in practice. However, a significant problem of using them is related with premature convergence of the computational algorithm due to incomplete exploration of the search space. This happens when all particles come into space of the first found, perhaps local optimum and cannot get out of it. To solve the problem, it is necessary to develop control procedures correcting movements of the individuals in the population. This paper proposes a particle swarm optimization adaptive modification, permitting dynamic changes to the particles’ trajectory to find more promising locations. The method is based on the opportunity to change the displacement vector individually for each particle depending on previous iteration effectiveness. For this purpose, procedures of direction choice and dynamic change of particle movement free parameters are added in the proposed modification. As opposed to the canonic swarm algorithm version, where all individuals converge on one particle with the best value found, in the new modification each particle chooses its displacement direction independently and can change it if the direction will be identified as ineffective. This approach makes it possible to reduce the probability of premature convergence of the algorithm and to explore given search space better, all of which is especially important for the multimodal function with complex landscape. The proposed method was tested on the standard set of test functions for continuous optimization, and it showed high reliability with relatively small use of time and computer resources.

Design Optimization of a Five-Phase IPM Synchronous Motor for Low-Speed Applications

In this paper a permanent magnet synchronous motor (PMSM) is designed for low-speed applications. These motors have recently been used in various industries, especially in military and transportation applications due to such features as high efficiency, high torque in low-speed range, high fault tolerance and reliability in addition to low weight, volume, maintenance cost and torque ripple, making research on their performance optimization the subject of many studies in recent years. This paper presents an analytical model of this machine with radially embedded magnets called spoke type used in the propulsion of ships and submarines. To increase the efficiency and reduce the volume and cost simultaneously, a multi-objective optimization algorithm based on the unified particle swarm method has been implemented which has led to enhanced values of efficiency, volume and cost. Finite element analysis has been done to verify the theoretically optimized design with the help of Ansys-Maxwell® software.

Optimization of the shell compliance by the thickness changing using PSO

The paper is devoted to an application of the particle swarm methods and the finite element method to the optimization of 2-D structures (plane stress/strain, bending plates and shells). The shape, topology and material or thickness of the structures are optimised for the stress, strain and volume criteria. The numerical examples demonstrate that the method based on particle swarm computation is an effective technique for solving computer aided optimal design.

A novel particle swarm and genetic algorithm hybrid method for diesel engine performance optimization

Heuristic methods have been a successful tool for optimizing engine parameters in both simulation and experimental testing. An improved hybrid method applying both the particle swarm optimization method and genetic algorithm was developed, tested, and compared with a basic particle swarm method for improving engine emissions and performance. A computational comparison between the particle swarm optimization–genetic algorithm hybrid, basic particle swarm optimization, and basic genetic algorithm was done using standard test problems. Computational results indicated improvements in both the efficiency and effectiveness of the present hybrid method. Engine testing was performed under steady-state conditions at 1400 r/min at 4.15 bar brake mean effective pressure. The basic particle swarm optimization and the hybrid particle swarm optimization–genetic algorithm method were applied to the test apparatus and used to locate the optimum neighborhood of the engine operation. A single-objective function representing NOx, particulate matter, hydrocarbon, CO, and fuel consumption was used in this application. The hybrid method was able to locate a narrow window of operation which showed 27% lower NOx emissions and 60% lower particulate matter emissions than the standard particle swarm optimization method. The hybrid method was able to locate the improvements using similar dynamometer time, indicating that the hybrid method is more efficient and more effective. Trends relating combustion characteristics and input parameters were observed and are discussed with regard to future improvements to heuristic methods for optimizing diesel engine performance.

Estimating Retention Times for Carbamates Pesticides by Training Bee's Swarm-Particles

The analysis of pesticide residues in food has become, very important in recent years because of the damage they may cause in human health. The analysis of residues requires, different equipment and software for the study, characterization, identification and quantification of these chemicals. However not always the desired parameters according to the method implemented are obtained, so the estimation of these is an optimal solution, considering the short runtime. Parameter estimation, is a tool used heavily in different fields and applications, in order to obtain, an unknown value from variables related. Estimating pesticide retention times Carbamates, by a trained neural network by the method of particle swarm, is presented in this work. The data for network training were acquired through a study of levels of pesticide residues present in samples of kidney tomato (Lycopersicon esculentum).

GLOBAL OPTIMIZATION OF FUNCTIONS OF SEVERAL VARIABLES USING PARALLEL TECHNOLOGIES

In this paper, on the basis of the method particle swarm optimization was de- veloped the algorithm of the parallel search of global extremum. In the system of parallel programming on C language implemented method of particle swarm for the global mini- mization of functions. The performance of the parallel method was tested for two famous benchmark optimization problems (Styblinski Tang function and Rastrigin function) and compared with the results obtained by employing the sequential method. Conducted research on the effectiveness of parallelization has shown the advantage of the parallel algorithm.

Evolutionary approaches to signal decomposition in an application service management system

The increased demand for autonomous control in enterprise information systems has generated interest on efficient global search methods for multivariate datasets in order to search for original elements in time-series patterns, and build causal models of systems interactions, utilization dependencies, and performance characteristics. In this context, activity signals deconvolution is a necessary step to achieve effective adaptive control in Application Service Management. The paper investigates the potential of population-based metaheuristic algorithms, particularly variants of particle swarm, genetic algorithms and differential evolution methods, for activity signals deconvolution when the application performance model is unknown a priori. In our approach, the Application Service Management System is treated as a black- or grey-box, and the activity signals deconvolution is formulated as a search problem, decomposing time-series that outline relations between action signals and utilization-execution time of resources. Experiments are conducted using a queue-based computing system model as a test-bed under different load conditions and search configurations. Special attention was put on high-dimensional scenarios, testing effectiveness for large-scale multivariate data analyses that can obtain a near-optimal signal decomposition solution in a short time. The experimental results reveal benefits, qualities and drawbacks of the various metaheuristic strategies selected for a given signal deconvolution problem, and confirm the potential of evolutionary-type search to effectively explore the search space even in high-dimensional cases. The approach and the algorithms investigated can be useful in support of human administrators, or in enhancing the effectiveness of feature extraction schemes that feed decision blocks of autonomous controllers.