Particle swarm optimization

Entropy, 2020

The Particle Swarm Optimisation (PSO) algorithm was inspired by the social and biological behaviour of bird flocks searching for food sources. In this nature-based algorithm, individuals are referred to as particles and fly through the search space seeking for the global best position that minimises (or maximises) a given problem. Today, PSO is one of the most well-known and widely used swarm intelligence algorithms and metaheuristic techniques, because of its simplicity and ability to be used in a wide range of applications. However, in-depth studies of the algorithm have led to the detection and identification of a number of problems with it, especially convergence problems and performance issues. Consequently, a myriad of variants, enhancements and extensions to the original version of the algorithm, developed and introduced in the mid-1990s, have been proposed, especially in the last two decades. In this article, a systematic literature review about those variants and improvemen...

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.

Journal of The Institution of Engineers (India): Series B, 2018

This paper presents an overview of the research progress in Particle Swarm Optimization (PSO) during 1995-2017. Fifty two papers have been reviewed. They have been categorized into nine categories based on various aspects. This technique has attracted many researchers because of its simplicity which led to many improvements and modifications of the basic PSO. Some researchers carried out the hybridization of PSO with other evolutionary techniques. This paper discusses the progress of PSO, its improvements, modifications and applications.

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.

2011

Particle Swarm Optimization (PSO) is a biologically inspired computational search and optimization method developed in 1995 by Eberhart and Kennedy based on the social behaviors of birds flocking or fish schooling. A number of basic variations have been developed due to improve speed of convergence and quality of solution found by the PSO. On the other hand, basic PSO is more appropriate to process static, simple optimization problem. Modification PSO is developed for solving the basic PSO problem. The observation and review 46 related studies in the period between 2002 and 2010 focusing on function of PSO, advantages and disadvantages of PSO, the basic variant of PSO, Modification of PSO and applications that have implemented using PSO. The application can show which one the modified or variant PSO that haven't been made and which one the modified or variant PSO that will be developed.

2012

Meta-heuristic optimization algorithms have become popular choice for solving complex and intricate problems which are otherwise difficult to solve by traditional methods. In the present study an attempt is made to review the one main algorithm is a well known meta-heuristic; Particle Swarm Optimization (PSO). PSO, in its present form, has been in existence for roughly a decade, a relatively short time compared with some of the other natural computing paradigms such as artificial neural networks and evolutionary computation. However, in that short period, PSO has gained widespread appeal amongst researchers and has been shown to offer good performance in a variety of application domains, with potential for hybridization and specialization, and demonstration of some interesting emergent behavior. This study comprises a snapshot of particle swarm optimization from the authors' perspective, including variations in the algorithm, modifications and refinements introduced to prevent swarm stagnation and hybridization of PSO with other heuristic algorithms.

2016

Abstract: Particle swarm optimization is a population-based, meta-heuristic optimization technique based on intelligence of swarm. The research on flock of birds or fish has been the motivation for this algorithm. Since this algorithm is easy to implement and requires few particles for tuning, this has been used widely nowadays. The main idea of this paper is to present the principle of PSO, improved PSO and research situation and the scope of future research.

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.

… , 1999. CEC 99. Proceedings of the 1999 …, 1999

Abstract-In this paper, we empirically study the performance of the particle swarm optimizer (PSO). Four different benchmark functions with asymmetric initial range settings are selected as testing functions. The experimental results illustrate the advantages and ...

Particle swarm optimization is a heuristic global optimization method put forward originally by Doctor Kennedy and Eberhart in 1995. Various efforts have been made for solving unimodal and multimodal problems as well as two dimensional to multidimensional problems. Efforts were put towards topology of communication, parameter adjustment, initial distribution of particles and efficient problem solving capabilities. Here we presented detail study of PSO and limitation in present work. Based on the limitation we proposed future direction. I. INTRODUCTION Swarm Intelligence (SI) is an innovative distributed intelligent paradigm for solving optimization problems that originally took its inspiration from the biological examples by swarming, flocking and herding phenomena in vertebrates. Particle Swarm Optimization (PSO) incorporates swarming behaviors observed in flocks of birds, schools of fish, or swarms of bees, and even human social behavior, from which the idea is emerged. PSO is a population-based optimization tool, which could be implemented and applied easily to solve various function optimization problems, or the problems that can be transformed to function optimization problems. As an algorithm, the main strength of PSO is its fast convergence, which compares favorably with many global optimization algorithms like Genetic Algorithms (GA), Simulated Annealing (SA) and other global optimization algorithms. While population-based heuristics are more costly because of their dependency directly upon function values rather than derivative information, they are however susceptible to premature convergence, which is especially the case when there are many decision variables or dimensions to be optimized. Particle swarm optimization is a heuristic global optimization method put forward originally by Doctor Kennedy and Eberhart in 1995. While searching for food, the birds are either scattered or go together before they locate the place where they can find the food. While the birds are searching for food from one place to another, there is always a bird that can smell the food very well, that is, the bird is perceptible of the place where the food can be found, having the better food resource information. Because they are transmitting the information, especially the good information at any time while searching the food from one place to another, conduced by the good information, the birds will eventually flock to the place where food can be found. As far as particle swam optimization algorithm is concerned, solution swam is compared to the bird swarm, the birds' moving from one place to another is equal to the development of the solution swarm, good information is equal to the most optimist solution, and the food resource is equal to the most optimist solution during the whole course. The most optimist solution can be worked out in particle swarm optimization algorithm by the cooperation of each individual. The particle without quality and volume serves as each individual, and the simple behavioral pattern is regulated for each particle to show the complexity of the whole particle swarm. In PSO, the potential solution called particles fly through the problem space by following the current optimum particles. Each particles keeps tracks of its coordinates in the problem space which are associated with the best solution (fitness) achieved so far. This value is called as pbest. Another best value that is tracked by the particle swarm optimizer is the best value, obtained so far by any particle in the neighbors of the particle. This value is called lbest. When a particle takes all the population as its topological neighbors, the best value is a global best and is called gbest. The particle swarm optimization concept consists of, at each time step, changing the velocity of (accelerating) each particle toward its pbest and lbest (for lbest version). Acceleration is weighted by random term, with separate random numbers being generated for acceleration towards pbest and lbest locations. After finding the best values, the particle updates its velocity and positions with following equations.