A Statistical Study of the Effects of Neighborhood Topologies in Particle Swarm Optimization

Abstract

Abstract. The behavior of modern meta-heuristics is directed by both, the variation operators, and the values selected for the parameters of the approach. Particle swarm optimization (PSO) is a meta-heuristic which has been found to be very successful in a wide variety of optimization tasks. In PSO, a swarm of particles fly through hyper-dimensional search space being attracted by both, their personal best position and the best position found so far within a neighborhood.In this paper, we perform a statistical study in order to analyze whether the neighborhood topology promotes a convergence acceleration in four PSO-based algorithms: the basic PSO, the Bare-bones PSO, an extension of BBPSO and the Bare-bones Differential Evolution. Our results indicate that the convergence rate of a PSO-based approach has a strongly dependence of the topology used. We also found that the topology most widely used is not necessarily the best topology for every PSO-based algorithm.

Unit Commitment Solution Using Particle Swarm Optimisation (PSO)

“…The idea may be considered as a generalization of classical set theory. UC is a complex decision-making process [1,3,5]which operates appropriate units at different hours and schedules the outputs of the committed units to meet a predicted demand, such that the operating cost is minimized. Due to the uncertainty of the demand and outages of generating units, fuzzy is used to represent the uncertainty.The method is an intelligence based technique that quantifies linguistic terms so that variables are treated as continuous.…”

“…It establishes the relation between input and output according to some fuzzy control rules.eg by using -if-then‖. The result is defuzzified to obtain numerical solution [4].But the disadvantage of this method is that it cannot handle large scale systems [5].…”

“…Unit commitment can be defined as the selection of generators that must be operated to meet the forecasted load demand on the system over a period of time so that fuel cost is minimum [2,3,4,5,6,7,8]. The Unit Commitment Problem (UCP) is to determine a minimal cost turn-on and turn-off schedule of a set of electrical power generating units to meet a load demand [23] while satisfying a set of operational constraints.It is a well known problem in power industry and helps in saving fuel cost if units are committed correctly so that fuel cost is saved.…”

“…In restructured market, small change in total cost can produce big changes in annual fuel cost. A literature survey on unit commitment reveals that several methods have been developed to solve unit commitment [1,2,3,4,5].They include…”

Task Scheduling in Distributed Systems Using Heap Intelligent Discrete Particle Swarm Optimization

“…Gregorio et al (Toscano‐Pulido et al, ; Reyes Medina et al, ) analyzed whether the neighborhood topology (fully connected, ring, star, VN, and tree) affects a convergence acceleration in PSO‐based algorithms such as the basic PSO, the Bare‐bones PSO, an extension of BBPSO, and the Bare‐bones DE. The results in Toscano‐Pulido et al () and Reyes Medina et al () indicate that the topology used in PSO‐based algorithms significantly affect the convergence rate and also found that the topology most widely used is not necessarily the best topology for every PSO‐based algorithms. Hamdan () proposed Hybrid PSO, which combines the idea of Star, Ring, and VN topological models in order to obtain the benefits of strength of all the models.…”