The Effect of Rearrangement of the Most Incompatible Particle on Increase of Convergence Speed of PSO

This article presents a new method for increasing the speed of Particle Swarm Optimization (PSO) method. The particle swarm is an optimization method that was inspired by collective movement of birds and fish looking for food. This method is composed of a group of particles: each particle tries to move in one direction that the best individual and best group of particles occur in that direction. Different articles tried to expand PSO so that global optimization is gained in less time. One of the problems of this model that occurs in most cases is falling of particles in local optimum. By finding the most incompatible particle and its rearrangement in the searching space, we increase convergence speed in some considered methods. Different tests of this method in standard searching space demonstrated that this method takes account of suitable function of increasing the convergece speed of particles.DOI:http://dx.doi.org/10.11591/ijece.v3i2.202

PSO for multi-objective problems: Criteria for leader selection and uniformity distribution

This paper proposes a method to solve multi-objective problems using improved Particle Swarm Optimization. We propose leader particles which guide other particles inside the problem domain. Two techniques are suggested for selection and deletion of such particles to improve the optimal solutions. The first one is based on the mean of the m optimal particles and the second one is based on appointing a leader particle for any n founded particles. We used an intensity criterion to delete the particles in both techniques. The proposed techniques were evaluated based on three standard tests in multi-objective evolutionary optimization problems. The evaluation criterion in this paper is the number of particles in the optimal-Pareto set, error, and uniformity. The results show that the proposed method searches more number of optimal particles with higher intensity and less error in comparison with basic MOPSO and SIGMA and CMPSO and NSGA-II and microGA and PAES and can be used as proper techniques to solve multi-objective optimization problems

Inteligencia de enjambres: sociedades para la solución de problemas (una revisión)

En este artículo se presenta una revisión de los conceptos de inteligencia de enjambres, y algunas perspectivas en la investigación con estas técnicas, con el objetivo de establecer un punto de partida para trabajos futuros en diferen-tes áreas de la ingeniería. Para la construcción de esta revisión se llevó a cabo una búsqueda bibliográfica en las bases de datos más actualizadas de los artículos clásicos del tema y de las últimas aplicaciones y resultados publi-cados, en particular en las áreas de control automático, procesamiento de señales e imágenes, y robótica, extra-yendo su concepto más relevante y organizándolo de manera cronológica. Como resultado se obtuvo taxonomía de la computación evolutiva, la diferencia entre la inteligencia de enjambres y otros algoritmos evolutivos, y una vi-sión amplia de las diferentes técnicas y aplicaciones.This paper presents a review of the basic concepts of swarm intelligence and some views regarding the future of re-search in this area aimed at establishing a starting point for future work in different engineering fields. A bibliogra-phic search of the most updated databases regarding classic articles on the subject and the most recent applications and results was used for constructing this review, especially in the areas of automatic control, signal and image pro-cessing and robotics. The main concepts were selected and organised in chronological order. A taxonomy was ob-tained for evolutionary computing techniques, a clear differentiation between swarm intelligence and other evolutio-nary algorithms and an overview of the different techniques and applications

Evolutionary swarm algorithm for modelling and control of horizontal flexible plate structures

Numerous advantages offered by the horizontal flexible structure have attracted increasing industrial applications in many engineering fields particularly in the airport baggage conveyor system, micro hand surgery and semiconductor manufacturing industry. Nevertheless, the horizontal flexible structure is often subjected to disturbance forces as vibration is easily induced in the system. The vibration reduces the performance of the system, thus leading to the structure failure when excessive stress and noise prevail. Following this, it is crucial to minimize unwanted vibration so that the effectiveness and the lifetime of the structure can be preserved. In this thesis, an intelligent proportional-integral-derivative (PID) controller has been developed for vibration suppression of a horizontal flexible plate structure. Initially, a flexible plate experimental rig was designed and fabricated with all clamped edges boundary conditions at horizontal position. Then, the data acquisition and instrumentation systems were integrated into the experimental rig. Several experimental procedures were conducted to acquire the input-output vibration data of the system. Next, the dynamics of the system was modeled using linear auto regressive with exogenous, which is optimized with three types of evolutionary swarm algorithm, namely, the particle swarm optimization (PSO), artificial bee colony (ABC) and bat algorithm (BAT) model structure. Their effectiveness was then validated using mean squared error, correlation tests and pole zero diagram stability. Results showed that the PSO algorithm has superior performance compared to the other algorithms in modeling the system by achieving lowest mean squared error of 6103947.4 , correlation of up to 95 % confidence level and good stability. Next, five types of PID based controllers were chosen to suppress the unwanted vibration, namely, PID-Ziegler Nichols (ZN), PID-PSO, PID-ABC, Fuzzy-PID and PID-Iterative Learning Algorithm (ILA). The robustness of the controllers was validated by exerting different types of disturbances on the system. Amongst all controllers, the simulation results showed that PID tuned by ABC outperformed other controllers with 47.60 dB of attenuation level at the first mode (the dominant mode) of vibration, which is equivalent to 45.99 % of reduction in vibration amplitude. By implementing the controllers experimentally, the superiority of PID-ABC based controller was further verified by achieving an attenuation of 23.83 dB at the first mode of vibration and 21.62 % of reduction in vibration amplitude. This research proved that the PID controller tuned by ABC is superior compared to other tuning algorithms for vibration suppression of the horizontal flexible plate structure

Genetic and particle swarm algorithm application to tire model fitting

TCC (graduação) - Universidade Federal de Santa Catarina. Campus Joinville. Engenharia Automotiva.The tire is the primary source of forces and torques that provide control and stability to the vehicle. Thus, the performance of a vehicle is mainly influenced by the characteris- tics of its tires. It is vital that the automotive engineer has a mathematical/ computational tool that provides precision and consistency to model a tire. The Magic Formula is the main model currently in use in the automotive industry. The fitting process of the Magic Formula model is a complex task and can be treated as a optimization problem. For this reason a particle swarm and genetic algorithm are implemented. A benchmark and comparison is made between these two algorithms, for standard test functions and fitting of a Magic Formula 6.1 model.É vital que o engenheiro automotivo tenha uma ferramenta matemática/computacional que forneça precisão e consistência para modelar um pneu. A Fórmula Mágica é o principal modelo atualmente em uso na indústria automotiva. O processo de ajuste do modelo Magic Formula é uma tarefa complexa e pode ser tratada como um problema de otimização. Por esta razão, um enxame de partículas e um algoritmo genético são implementados. Um benchmark e uma comparação são feitos entre esses dois algoritmos, para funções de teste padrão e ajuste de um modelo Magic Formula 6.1

Step-Optimized Particle Swarm Optimization

Particle swarm optimization (PSO) is widely used in industrial and academic research to solve optimization problems. Recent developments of PSO show a direction towards adaptive PSO (APSO). APSO changes its behaviour during the optimization process based on information gathered at each iteration. It has been shown that APSO is able to solve a wide range of difficult optimization problems efficiently and effectively. In classical PSO, all parameters are fixed for the entire swarm. In particular, all particles share the same settings of their velocity weights. We propose four APSO variants in which every particle has its own velocity weights. We use PSO to optimize the settings of the velocity weights of every particle at every iteration, thereby creating a step-optimized PSO (SOPSO). We implement four known PSO variants (global best PSO, decreasing weight PSO, time-varying acceleration coefficients PSO, and guaranteed convergence PSO) and four proposed APSO variants (SOPSO, moving bounds SOPSO, repulsive SOPSO, and moving bound repulsive SOPSO) in a PSO software package. The PSO software package is used to compare the performance of the PSO and APSO variants on 22 benchmark problems. Test results show that the proposed APSO variants outperform the known PSO variants on difficult optimization problems that require large numbers of function evaluations for their solution. This suggests that the SOPSO strategy of optimizing the settings of the velocity weights of every particle improves the robustness and performance of PSO

Estratégias evolucionárias de optimização de parâmetros reais

Mestrado em Engenharia MecânicaActualmente, existem diversos problemas de engenharia cujas propriedades podem ser expressas através de uma função, denominada função objectivo. Existem diversos métodos que possuem como principal objectivo minimizar a referida função. Os métodos baseados no gradiente são métodos nos quais a direcção e tamanho do passo são calculados a partir do declive da função objectivo. Apesar destes métodos necessitarem de reduzidos tempos de computação, estes podem convergir prematuramente ou ficar estagnados em mínimos locais. Os métodos de optimização baseados na teoria evolucionária são aproximações que possuem como principal desvantagem elevados tempos de computação. No entanto, estes apresentam uma grande flexibilidade na modelação de problemas de engenharia. Neste grupo, os algoritmos mais conhecidos e aplicados em problemas de optimização são os Algoritmos Evolucionários (EA’s), Algoritmos Genéticos (GA’s) e Evolução Diferencial (DE). Existem ainda algoritmos baseados em processos naturais tais como o algoritmo de Optimização por Bandos de Partículas (PSO), que reproduz o comportamento de bandos de animais. Neste trabalho é desenvolvido um algoritmo de optimização de procura directa baseado em métodos diferenciais, evolucionários e no comportamento de animais. O algoritmo desenvolvido é aplicado a problemas de engenharia inversa. Numa primeira fase, o algoritmo desenvolvido é validado e comparado com algoritmos existentes recorrendo a um conjunto de funções compostas especialmente criadas para este fim. Numa segunda fase, o algoritmo desenvolvido é aplicado a problemas de Engenharia Mecânica e Mecânica Computacional. Nesta secção, os problemas das três barras e da cúpula de 120 barras são analisados recorrendo ao Método dos Elementos Finitos (MEF). Seguidamente, os problemas de compressão de um provete cilíndrico e da placa com furo central são analisados. Nestes problemas a função a minimizar é dada por um programa do MEF comercial. Finalmente, o algoritmo é aplicado a um problema de identificação de parâmetros de um modelo constitutivo. O algoritmo desenvolvido apresenta bons resultados e uma boa taxa de convergência. ABSTRACT: Nowadays, there are many inverse engineering problems whose properties can be expressed by a function, called objective function. There are several methods whose main goal is to minimize the value of that function. The gradient-based methods are optimization methods in which the step direction and length are calculated in terms of the objective function's slope. Although these methods require little computation time, they may converge prematurely or get trapped in a local minima. The optimization methods based on the evolutionary theory are approaches that need, as a main disadvantage, high computation times. However, they have a great flexibility in modeling engineering problems. In this class of methods, the ones that are best known and more often applied in optimization problem are the Evolutionary Algorithms (EA's), Genetic Algorithms (GA's) and Differential Evolution (DE). There are also nature-inspired algorithms such as the Particle Swarm Optimization method (PSO) that mimics the behavior of animal swarms. In this work a direct search optimization algorithm based on differential and evolutionary methods as well as in the behavior of animals is developed. This algorithm is applied to inverse engineering problems. In a first stage, the developed algorithm is validated and compared with existing optimization algorithms using a set of composite functions specially design for that purpose. In a second phase, the algorithm is applied to Engineering and Computational Mechanics problems. In this section, the three-truss bar problem and the 120-bar dome truss problem that are solved using the Finite Element Method (FEM) are analyzed. Subsequently, the compression of a cylindrical billet and the plate with a central cut-out problems are analyzed. In these problems, the function to minimize is given by a commercial FEM code. Finally, the algorithm is applied to a constitutive model parameter identification problem. The develop algorithm obtains good results and a good convergence rate

Algoritmo de optimización basado en enjambres de partículas con comportamiento de vorticidad Vortex Particle Swarm Optimization VPSO

En este documento se propone un algoritmo de optimización basado en el movimiento de partículas con características de vorticidad. Uno de los principales problemas que se presenta en optimización es la convergencia temprana a mínimos locales. El algoritmo propuesto emplea un modelo de partículas activas con desplazamientos circulares lo cual permite escapar de mínimos locales. También se realiza un análisis del modelo seleccionado, comprobando mediante simulaciones que los resultados obtenidos son consistentes con el comportamiento del sistema. El algoritmo propuesto se probó en casos estándar obteniendo un desempeño satisfactorioAbstract. This document proposes an optimization algorithm based on the motion of particles with vortex behavior. A main disadvantage in optimization is the early convergence to local minima. The proposed algorithm uses an active particle model with circular behaviors, which allows escaping from local minima. An approximate analysis of the selected model was performed and it was observed, via simulations, that the results obtained were consistent with the behavior of the system. The proposed algorithm is tested on benchmark problems obtaining satisfactory performance.Doctorad