Different Applications of PSO (original) (raw)

A New Taxonomy for Particle Swarm Optimization (PSO)

The Particle Swarm Optimization (PSO) algorithm, as one of the latest algorithms inspired from the nature, was introduced in the mid 1995, and since then has been utilized as a powerful optimization tool in a wide range of applications. In this paper, a general picture of the research in PSO is presented based on a comprehensive survey of about 1800 PSO-related papers published from 1995 to 2008. After a brief introduction to the PSO algorithm, a new taxonomy of PSO-based methods is presented. Also, 95 major PSO-based methods are introduced and their parameters summarized in a comparative table. Finally, a timeline of PSO applications is portrayed which is categorized into 8 main fields.

IJERT-Study and Analysis of Particle Swarm Optimization and Its Implementation

International Journal of Engineering Research and Technology (IJERT), 2014

https://www.ijert.org/study-and-analysis-of-particle-swarm-optimization-and-its-implementation https://www.ijert.org/research/study-and-analysis-of-particle-swarm-optimization-and-its-implementation-IJERTV3IS070906.pdf This paper is basically about Particle Swarm optimization technique which is basically an optimization technique .My paper focuses on the aspect that how PSO is a better optimization technique and the reason behind the increasing usage of PSO nowadays. There will also be a brief discussion on the algorithm and its implementation. Particle Swarm optimization is a heuristic global optimization method .This works on a particular search space .Optimization is a mechanism of finding minimum and maximum values from a given set of values. This is done by taking some particular parameters and calculating results according to it. This technique was first discovered by James Kennedy and Russell C. Eberhart in 1995. [1] This technique is widely used because of its easy and fast implementation in comparison to other techniques. Idea of PSO is originated from two separate concepts-the first one is swarm intelligence and second one is evolutionary computation.

Analysis of the publications on the applications of particle swarm optimisation

Journal of Artificial Evolution and Applications, 2008

Particle swarm optimisation (PSO) has been enormously successful. Within little more than a decade hundreds of papers have reported successful applications of PSO. In fact, there are so many of them, that it is difficult for PSO practitioners and researchers to have a clear up-to-date vision of what has been done in the area of PSO applications. This brief paper attempts to fill this gap, by categorising a large number of publications dealing with PSO applications stored in the IEEE Xplore database at the time of writing.

Overview of Particle Swarm Optimization ( PSO ) on its Applications and Methods

2013

Particle Swarm Optimization (PSO) that is famous as a heuristic robust stochastic optimization technique works in field of Artificial Intelligence (AI). This technique of optimization is inspired by certain behaviors of animals such as bird flocking. The base of PSO method is on swarm intelligence that has a huge effect on solving problem in social communication. Hence, the PSO is a useful and valuable technique with goal of maximizing or minimizing of certain value that has been used in wide area and different fields such as large field of engineering, physics, mathematics, chemistry and etc. in this paper, following a brief introduction to the PSO algorithm, the method of that is presented and it’s important factors and parameters are summarized. The main aim of this paper is to overview, discuss of the available literature of the PSO algorithm yearly.

A Study on Various Particle Swarm Optimization Techniques used in Current Scenario

Design, Modelling and Fabrication of Advanced Robots, 2022

optimization, that optimizes a problem by iteratively trying to improve a candidate solution with regard to a given measure of quality. The book by Kennedy and Bernhard describes many philosophical aspects of PSO and swarm intelligence. The Disadvantages of the particle mass optimization (PSO) algorithm are that it is easy to fall locally optimized at high dimensional space and has a low integration rate in the recirculation process. The computational complexity of DWCNPSO is accepted when used to solve high dimensional and complex problems. Particle mass optimization (PSO) is one of the bio-inspired algorithms, and finding the optimal solution in place of the solution is a simple one. It differs from other upgrade algorithms in that it requires only objective functionality and is not subject to gradient or objective particle mass optimization It does not depend on any different form, as proposed in the paper, as mentioned in the original, sociologists believe that At the school of fish or in a group A flock of migratory birds can "benefit from the experience of all other members." In other words, when a bird flies and randomly searches for food, for example, all the birds in the herd can share their findings and help the whole flock to hunt better.

Tutorial on particle swarm optimization and its combinations to other evolutionary algorithms

Open Science Framework (OSF) Preprints, 2022

Local optimization with convex function is solved perfectly by traditional mathematical methods such as Newton-Raphson and gradient descent but it is not easy to solve the global optimization with arbitrary function although there are some purely mathematical approaches such as approximation, cutting plane, branch and bound, and interval method which can be impractical because of their complexity and high computation cost. Recently, some evolutional algorithms which are inspired from biological activities are proposed to solve the global optimization by acceptable heuristic level. Among them is particle swarm optimization (PSO) algorithm which is proved as an effective and feasible solution for global optimization in real applications. Although the ideology of PSO is not complicated, it derives many variants, which can make new researchers confused. Therefore, this tutorial focuses on describing, systemizing, and classifying PSO by succinct and straightforward way. Moreover, combinations of PSO and other evolutional algorithms for improving PSO itself or solving other advanced problems are mentioned too.

(2006) Particle Swarm Optimization: Development of a General-Purpose Optimizer

For problems where the quality of any solution can be quantified in a numerical value, optimization is the process of finding the permitted combination of variables in the problem that optimizes that value. Traditional methods present a very restrictive range of applications, mainly limited by the features of the function to be optimized and of the constraint functions. In contrast, evolutionary algorithms present almost no restriction to the features of these functions, although the most appropriate constraint-handling technique is still an open question. The particle swarm optimization (PSO) method is sometimes viewed as another evolutionary algorithm because of their many similarities, despite not being inspired by the same metaphor. Namely, they evolve a population of individuals taking into consideration previous experiences and using stochastic operators to introduce new responses. The advantages of evolutionary algorithms with respect to traditional methods have been greatly discussed in the literature for decades. While all such advantages are valid when comparing the PSO paradigm to traditional methods, its main advantages with respect to evolutionary algorithms consist of its noticeably lower computational cost and easier implementation. In fact, the plain version can be programmed in a few lines of code, involving no operator design and few parameters to be tuned. This paper deals with three important aspects of the method: the influence of the parameters’ tuning on the behaviour of the system; the design of stopping criteria so that the reliability of the solution found can be somehow estimated and computational cost can be saved; and the development of appropriate techniques to handle constraints, given that the original method is designed for unconstrained optimization problems.

Particle Swarm Optimization: Methods, Taxonomy and Applications

The Particle Swarm Optimization (PSO) algorithm, as one of the latest algorithms inspired from the nature, was introduced in the mid 1990s, and since then has been utilized as an optimization tool in various applications, ranging from biological and medical applications to computer graphics and music composition. In this paper, following a brief introduction to the PSO algorithm, the chronology of its evolution is presented and all major PSO-based methods are comprehensively surveyed. Next, these methods are studied separately and their important factors and parameters are summarized in a comparative table. In addition, a new taxonomy of PSO-based methods is presented. It is the purpose of this paper to present an overview of the previous and present status of PSO algorithms well as its opportunities and challenges. Accordingly, the history, various methods, and taxonomy of this algorithm are discussed and its different applications together with an analysis of these applications are evaluated.

Particle Swarm Optimization (PSO) and two real world applications

2019

Facultat de Matemàtiques i Informàtica MSc Particle Swarm Optimization (PSO) and two real world applications by Albert PRAT Particle Swarm Optimization (PSO) belongs to a powerful family of optimization techniques inspired by the collective behaviour of social animals. This method has shown promising results in a wide range of applications, especially in computer science. Despite this, a great popularity of such method has not been achieved. Since we believe in the potential of PSO, we propose the following scheme to be able to take advantage of its properties. First, an implementation from scratch in C language of the method has been done, as well as an analysis of its parameters and its performance in function minimization. Then, a second more specific part of this thesis is devoted to the adaptation of the method for solving two real-world applications. The first one, in the field of signal analysis, consists of an optimization method for the numerical analysis of Fourier functions, whereas the second, in the field of computer science, comprises the optimization of neural networks weights' for some small architectures. v Throughout the writing of this dissertation I have received a great deal of support and assistance. I would first like to thank my supervisor, Dr. Gerard Gómez, whose expertise in formulating the research topic, setting directives and suggesting methodology was unquestionable. Secondly, I would also thank Dr. Jordi Vitrià, for the opportunity given to develop a topic of our liking and for the guidance regarding formal issues. In addition, I thank Núria Valls, my partner in developing this thesis, for her invaluable help provided throughout all this dissertation: from the first moment you have listened to all my doubts and concerns, specially in developing the library and you have spent many hours teaching me C programming language. I have felt understood at every moment and your willingness stands out. Finally, I would also thank my sister, Dr. Judit Prat, Dr. Àlex Alarcón and Ramon Mir, for their support and patience: you have given me wise advice and emotional support.

Particle Swarm Optimization -A Tutorial

Optimization algorithms are necessary to solve many problems such as parameter tuning. Particle Swarm optimization (PSO) is one of these optimization algorithms. The aim of PSO is to search for the optimal solution in the search space. This paper highlights the basic background needed to understand and implement the PSO algorithm. This paper starts with basic definitions of the PSO algorithm and how the particles are moved in the search space to find the optimal or near optimal solution. Moreover, a numerical example is illustrated to show how the particles are moved in convex optimization problem. Another numerical example is illustrated to show how the PSO trapped in a local minima problem. Two experiments are conducted to show how the PSO searches for the optimal parameters in one-dimensional and two-dimensional spaces to solve machine learning problems.