LDWMeanPSO: A new improved particle swarm optimization technique (original) (raw)

Study and Analysis of Particle Swarm Optimization: A Review

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.

A Review of Particle Swarm Optimization

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.

A review on Particle Swarm Optimization Technique

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.

A New Particle Swarm Optimization Technique

In this paper, a new particle swarm optimization method (NPSO) is proposed. It is compared with the regular particle swarm optimizer (PSO) invented by Kennedy and Eberhart in 1995 based on four different benchmark functions. PSO is motivated by the social behavior of organisms, such as bird flocking and fish schooling. Each particle studies its own previous best solution to the optimization problem, and its group's previous best, and then adjusts its position (solution) accordingly. The optimal value will be found by repeating this process. In the NPSO proposed here, each particle adjusts its position according to its own previous worst solution and its group's previous worst to find the optimal value. The strategy here is to avoid a particle's previous worst solution and its group's previous worst based on similar formulae of the regular PSO. Under all test cases, simulation shows that the NPSO always finds better solutions than PSO.

MHPSO: A new method to enhance the Particle Swarm Optimizer

… (ICDIM), 2011 Sixth …, 2011

The widespread and increasing application of Particle Swarm Optimizer (PSO) algorithms in both theoretical and practical fields leads to further considerations and new developments for improving its efficiency. To achieve this purpose in this paper a new method is introduced to enhance the convergence rate and reduce the computational time of PSO by combining the PSO including mutation concept (MPSO) and the Hierarchical Particle Swarm Optimizer (HPSO). Therefore the new approach is called MHPSO: a composition of MPSO and HPSO which act simultaneously in the optimization process. In addition some benchmark examples are analyzed using the presented method; consequently, the results are compared to other procedures which illustrate better outcomes and high performance of MHPSO.

Particle Swarm Optimization: Technique, System and Challenges

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.

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.

An Overview of Particle Swarm Optimization Variants

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.

On Particle Swarm Optimization Algorithm

2023

The swarm of particle optimization algorithm is among the most important tools in finding the optimal solution to nonlinear optimization problems. The main goal of this research is an expanded study by developing an effective algorithm to find the optimal solution based on the speed of convergence. The study also included comparing the results with an algorithm with the same orientation, as well. The results showed the superiority of the developed algorithm based on the results obtained.

Improvement of Particle Swarm Optimization Using Personal Best Adaptive Weight

International Journal of Innovative Science and Research Technology, 2021

Improvement of the particle swarm optimization algorithm has become increasingly important to deliver it out of local optima trapping and increase its convergence rate. In this paper a personal best adaptive weight is proposed as a new PSO variant named personal best adaptive weight particle swarm optimization (PBAW-PSO) to choose different inertia weight for different particles in the swarm to update their velocity. The proposed variant was compared with three other inertia weight improved variants on six benchmark functions. The comparison was done based on the best cost, mean cost, simulation time, standard deviation and convergence rate. The overall results showed that the PBAW-PSO variant had a better performance than the other variants.