Particle Swarm Optimization -A Tutorial

Swarm Intelligence, 2007

Particle swarm optimization (PSO) has undergone many changes since its introduction in 1995. As researchers have learned about the technique, they have derived new versions, developed new applications, and published theoretical studies of the effects of the various parameters and aspects of the algorithm. This paper comprises a snapshot of particle swarming from the authors' perspective, including variations in the algorithm, current and ongoing research, applications and open problems.

Optimization is a mathematical technique that concerns the finding of maxima or minima of functions in some feasible region. There is no business or industry which is not involved in solving optimization problems. A variety of optimization techniques compete for the best solution. Particle Swarm Optimization (PSO) is a relatively new, modern, and powerful method of optimization that has been empirically shown to perform well on many of these optimization problems. It is widely used to find the global optimum solution in a complex search space. This thesis aims at providing a review and discussion of the most established results on PSO algorithm as well as exposing the most active research topics that can give initiative for future work and help the practitioner improve better result with little effort. This paper introduces a theoretical idea and detailed explanation of the PSO algorithm, the advantages and disadvantages, the effects and judicious selection of the various parameters. Moreover, this thesis discusses a study of boundary conditions with the invisible wall technique, controlling the convergence behaviors of PSO, discrete-valued problems, multi-objective PSO, and applications of PSO. Finally, this paper presents some kinds of improved versions as well as recent progress in the development of the PSO, and the future research issues are also given.

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.

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.

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 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.

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.

Preprints, 2021

Particle swarm optimization (PSO) is an effective algorithm to solve the optimization problem in case that derivative of target function is inexistent or difficult to be determined. Because PSO has many parameters and variants, I propose a general framework of PSO called GPSO which aggregates important parameters and generalizes important variants so that researchers can customize PSO easily. Moreover, two main properties of PSO are exploration and exploitation. The exploration property aims to avoid premature converging so as to reach global optimal solution whereas the exploitation property aims to motivate PSO to converge as fast as possible. These two aspects are equally important. Therefore, GPSO also aims to balance the exploration and the exploitation. It is expected that GPSO supports users to tune parameters for not only solving premature problem but also fast convergence.

Particle swarm optimization (PSO) is a computational method that optimizes a problem by iteratively trying to improve a candidate solution with regard to a given measure of quality. It solves a problem by having a population of candidate solutions, here dubbed particles, and moving these particles around in the search-space according to simple mathematical formulae over the particle's position and velocity. Each particle's movement is influenced by its local best-known position (pbest), but is also guided toward the best-known positions (gbest) in the search-space, which are updated as better positions are found by other particles. This is expected to move the swarm toward the best solutions. The particles move in the search space with considering its own velocity and position called as pbest, but pbest has the tendency to flow around the local optima. Because of this problem we compare the different Particle swarm optimization based algorithm with its principles & application in this paper. Variable Neighbourhood PSO, Adaptive PSO & Niche PSO compare to see the performance of the particles in the search space with respect to time.