Population Structure And Particle Swarm Performance Pdf

population structure and particle swarm performance pdf

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In computational science , particle swarm optimization PSO [1] 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.

A Performance Class-Based Particle Swarm Optimizer

Metrics details. Different mixtures of ion concentrations and temperatures generate almost identical backscattered signals, hindering the discrimination between plasma parameters. This temperature—ion composition ambiguity problem is commonly solved either by using models of ionospheric parameters or by the addition of parameters determined from the plasma line of the radar. Some studies demonstrated that it is also possible to unambiguously estimate ISR signals with very low signal fluctuation using the most frequently used non-linear least squares NLLS fitting algorithm. In a previous study, the unambiguous estimation performance of the particle swarm optimization PSO algorithm was evaluated, outperforming the standard NLLS algorithm fitting signals with very small fluctuations.

The particle swarm optimization PSO algorithm, in which individuals collaborate with their interacted neighbors like bird flocking to search for the optima, has been successfully applied in a wide range of fields pertaining to searching and convergence. Here we employ the scale-free network to represent the inter-individual interactions in the population, named SF-PSO. In contrast to the traditional PSO with fully-connected topology or regular topology, the scale-free topology used in SF-PSO incorporates the diversity of individuals in searching and information dissemination ability, leading to a quite different optimization process. Systematic results with respect to several standard test functions demonstrate that SF-PSO gives rise to a better balance between the convergence speed and the optimum quality, accounting for its much better performance than that of the traditional PSO algorithms. We further explore the dynamical searching process microscopically, finding that the cooperation of hub nodes and non-hub nodes play a crucial role in optimizing the convergence process. Our work may have implications in computational intelligence and complex networks. This is an open-access article distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

Particle Swarm Optimization PSO is a search method which utilizes a set of agents that move through the search space to find the global minimum of an objective function. The trajectory of each particle is determined by a simple rule incorporating the current particle velocity and exploration histories of the particle and its neighbors. Since its introduction by Kennedy and Eberhart in , PSO has attracted many researchers due to its search efficiency even for a high dimensional objective function with multiple local optima. The dynamics of PSO search has been investigated and numerous variants for improvements have been proposed. This paper reviews the progress of PSO research so far, and the recent achievements for application to large-scale optimization problems.

Human Behavior-Based Particle Swarm Optimization

Particle swarm optimization PSO has attracted many researchers interested in dealing with various optimization problems, owing to its easy implementation, few tuned parameters, and acceptable performance. However, the algorithm is easy to trap in the local optima because of rapid losing of the population diversity. Therefore, improving the performance of PSO and decreasing the dependence on parameters are two important research hot points. First, the global worst particle was introduced into the velocity equation of PSO, which is endowed with random weight which obeys the standard normal distribution; this strategy is conducive to trade off exploration and exploitation ability of PSO. Second, we eliminate the two acceleration coefficients and in the standard PSO SPSO to reduce the parameters sensitivity of solved problems.

PDF | On Jan 1, , Carlos M. Fernandes and others published Revisiting Population Structure and Particle Swarm Performance | Find, read.

Multi-population Cooperative Particle Swarm Optimization

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Human Behavior-Based Particle Swarm Optimization

One of the main concerns with Particle Swarm Optimization PSO is to increase or maintain diversity during search in order to avoid premature convergence. In this study, a Performance Class-Based learning PSO PCB-PSO algorithm is proposed, that not only increases and maintains swarm diversity but also improves exploration and exploitation while speeding up convergence simultaneously. In the PCB-PSO algorithm, each particle belongs to a class based on its fitness value and particles might change classes at evolutionary stages or search step based on their updated position. The particles are divided into an upper, middle and lower. In the upper class are particles with top fitness values, the middle are those with average while particles in the bottom class are the worst performing in the swarm.

Abstract: Population structure strongl y affects the dynamic behavior and performance of the particle swarm. Most of PSOs use one of two simple soc iometric pr inciples for defining th e. One connects all the members of the swarm to one another.

Multi-population Cooperative Particle Swarm Optimization

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PDF | The effects of various population topologies on the particle swarm algorithm were systematically investigated. Random graphs were.

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