A Review of Particle Swarm Optimization

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.

research.ijcaonline.org

Particle swarm optimization is a global optimization algorithm that originally took its inspiration from the biological examples by swarming, flocking and herding phenomena in vertebrates. This paper presents a review on PSO in single and multiobjective optimization. The paper contains the basic PSO algorithm and various techniques used in pre-existing algorithms. It also describes the simulation result which is carried out on benchmark functions of single objective optimization with the help of basic PSO. Study of literature shows future direction to enhance the performance of 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.

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.

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...

2016

Particle Swarm Optimization (PSO) is a biologically inspired computational search and optimization method developed in 1995 by Eberhart and Kennedy based on the social behavior of bird flocking or fish schooling A number of basic variations developed by convergence speed and quality improvement solution are found. On the other hand, basic PSO is to handle the construction, simple optimization problem Modification PSO has been developed for solving the fundamental problem PSO. The observation and assessment 46 related studies in the period between 2002 and 2010 focused on the function of the PSO, advantages and disadvantages of PSO, the PSO basic variant and applications that are carried out using PSO. The PSO has tremendous applications in the power system too.

2014

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.

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.

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.

Procedia Engineering, 2013

Particle swarm optimization (PSO) is a stochastic algorithm used for the optimization problems proposed by Kennedy [1] in 199 5. It is a very good technique for the optimization problems. But still there is a drawback in the PSO is that it stuck in the local minima. To improve the performance of PSO, the researchers proposed the different variants of PSO. Some researchers try to improve it by improving initialization of the swarm. Some of them introduce the new parameters like constriction coefficient and inertia weight. Some researchers define the different method of inertia weight to improve the performance of PSO. Some researchers work on the global and local best particles by introducing the mutation operators in the PSO. In this paper, we will see the different variants of PSO with respect to initialization, inertia weight and mutation operators.