A New Particle Swarm Optimization Technique

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.

Particle swarm optimization is a heuristic global optimization method and also an optimization algorithm, which is based on swarm intelligence. It comes from the research on the bird and fish flock movement behavior. The algorithm is widely used and rapidly developed for its easy implementation and few particles required to be tuned. The main idea of the principle of PSO is presented; the advantages and the shortcomings are summarized. At last this paper presents some kinds of improved versions of PSO and research situation, and the future research issues are also given.

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.

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.

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.

2007

In this work, a set of operators for a Particle Swarm (PS) based optimization algorithm is investigated for the purpose of finding optimal values for some of the classical benchmark problems. Particle swarm algorithms are implemented as mathematical operators inspired by the social behaviors of bird flocks and fish schools. In addition, particle swarm algorithms utilize a small number of relatively uncomplicated rules in response to complex behaviors, such that they are computationally inexpensive in terms of memory requirements and processing time. In particle swarm algorithms, particles in a continuous variable space are linked with neighbors, therefore the updated velocity means of particles influences the simulation results. The paper presents a statistical investigation on the velocity update rule for continuous variable PS algorithm. In particular, the probability density function influencing the particle velocity update is investigated along with the components used to construct the updated velocity vector of each particle within a flock. The simulation results of several numerical benchmark examples indicate that small amount of negative velocity is necessary to obtain good optimal values near global optimality.

Swarm Intelligence (SI) describes the evolving collective intelligence of population/groups of autonomous agents with a low level of intelligence. Particle Swarm Optimization (PSO) is an evolutionary algorithm inspired by animal social behaviour. PSO achieves performance by iteratively directing its particles toward the optimum using its social and cognitive components. Various modifications have been applied to PSO focusing on addressing a variety of methods for adjusting PSO's parameters (i.e., parameter adjustment), social interaction of the particles (i.e., neighbourhood topology) and ways to address the search objectives (i.e., sub-swarm topology). The PSO approach can easily fit in different search optimization categories such as Self Learning, Unsupervised Learning, Stochastic Search, Population-based, and Behaviour-based Search. This study addresses these principal aspects of PSO. In addition, conventional and Basic PSO are introduced and their shortcomings are discussed. Later on, various suggestions and modifications proposed by literature are introduced and discussed.

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.

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.

Particle swarm optimization is a stochastic, population-based computer problem-solving algorithm; it is a kind of swarm intelligence that is based on social- principles and provides insights into social behavior, as well as contributing to social-psychological engineering applications. The aim of this paper is to give fundamental insight into the particle swarm optimization algorithm