Abstract: The effects of various population topologies on the particle swarm algorithm were systematically investigated. Random graphs were generated to specifications, and their performance on several criteria was compared. What makes a good population structure? We discovered that previous assumptions may not have been correct. I.

The canonical particle swarm algorithm is a new approach to optimization, drawing inspiration from group behavior and the establishment of social norms. It is gaining popularity, especially because of the speed of convergence and the fact it is easy to use. However, we feel that each individual is not simply influenced by the best performer among his neighbors. We thus decided to make the individuals “fully informed. ” The results are very promising, as informed individuals seem to find better solutions in all the benchmark functions.

This paper presents approaches for effectively computing all global minimizers of an objective function. The approaches include transformations of the objective function through the recently proposed deflection and stretching techniques, as well as a repulsion source at each detected minimizer. The aforementioned techniques are incorporated in the context of the particle swarm optimization (PSO) method, resulting in an efficient algorithm which has the ability to avoid previously detected solutions and, thus, detect all global minimizers of a function. Experimental results on benchmark problems originating from the fields of global optimization, dynamical systems, and game theory, are reported, and conclusions are derived.

A new optimization method has been proposed by Kennedy et. al. in [7, 8], called Particle Swarm Optimization (PSO). This approach combines social psychology principles in socio-cognition of human (and artificial) agents and evolutionary computation. It has been successfully applied to nonlinear function optimization and neural network training. Preliminary formal analyses for a simple PSO system show that a particle in a simple PSO system follows a path defined by a sinusoidal wave, randomly deciding on both its amplitude and frequency [12]. This paper takes the next step, generalizing to obtain closed form equations for trajectories of particles in a multi-dimensional search space. 1 Introduction Evolutionary computation techniques are search methods based on natural systems. For example, Genetic Algorithms (GAs) use principles of genetics and natural selection [4]. "Particle Swarm Optimization" (PSO) [7, 8] is a recently proposed algorithm, motivated by the behavior of organisms s...

This paper introduces a Multi-Objective Algorithm (MOA) based upon the Particle Swarm Optimisation (PSO) heuristic.

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A dissipative particle swarm optimization is developed according to the self-organization of dissipative structure. The negative entropy is introduced to construct an opening dissipative system that is far-from-equilibrium so as to driving the irreversible evolution process with better fitness. The testing of two multimodal functions indicates it improves the performance effectively.

A new dynamic clustering approach (DCPSO), based on Particle Swarm Optimization, is proposed. This approach is applied to unsupervised image classification. The proposed approach automatically determines the "optimum " number of clusters and simultaneously clusters the data set with minimal user interference. The algorithm starts by partitioning the data set into a relatively large number of clusters to reduce the effects of initial conditions. Using binary particle swarm optimization the "best" number of clusters is selected. The centers of the chosen clusters is then refined via the K-means clustering algorithm. The experiments conducted show that the proposed approach generally found the "optimum" number of clusters on the tested images.

In this paper we present two hybrid Particle Swarm Optimisers combining the idea of the particle swarm with concepts from Evolutionary Algorithms.

Many data mining applications involve the task of building a model for predictive classification. The goal of such a model is to classify examples (records or data instances) into classes or categories of the same type. The use of variables (attributes) not related to the classes can reduce the accuracy and reliability of a classification or prediction model. Superfluous variables can also increase the costs of building a model- particularly on large data sets. We propose a discrete Particle Swarm Optimization (PSO) algorithm designed for attribute selection. The proposed algorithm deals with discrete variables, and its population of candidate solutions contains particles of different sizes. The performance of this algorithm is compared with the performance of a standard binary PSO algorithm on the task of selecting attributes in a bioinformatics data set. The criteria used for comparison are: (1) maximizing predictive accuracy; and (2) finding the smallest subset of attributes. Categories and Subject Descriptors I.2.6 [Computing Methodologies]: Artificial Intelligence— Learning, induction