Specifically, we detail two methods which adopt the search and score approach to BN learning. The two algorithms are similar in that they both use PSO as the search algorithm, and the K2 metric to score the resulting network. The difference lies in the way networks are constructed. The CONstruct And Repair (CONAR) algorithm generates structures, validates, and repairs if required, and the REstr...

Particle swarm optimisation is a metaheuristic algorithm which finds reasonable solutions in a wide range of applied problems if suitable parameters are used. We study the properties of the algorithm in the framework of random dynamical systems which, due to the quasi-linear swarm dynamics, yields analytical results for the stability properties of the particles. Such considerations predict a re...

We de ne ve cooperation mechanisms in Particle Swarm Optimisation, loosely inspired by some models occurring in nature, and based on two quantities: a help matrix, and a reputation vector. We call these ve mechanisms, respectively, Reciprocity, Vicinity, Kin, Reputation, and Anybody. It appears that Kin is better than the rest by a slight margin, but needs more parameters that have to be tuned ...

in this article, multiple-product pvrp with pickup and delivery that is used widely in goods distribution or other service companies, especially by railways, was introduced. a mathematical formulation was provided for this problem. each product had a set of vehicles which could carry the product and pickup and delivery could simultaneously occur. to solve the problem, two meta-heuristic methods...

ABSTRACT. All PSO versions do present one or more biases, often in favor of the center of the search space. An important factor that induces such biases is the method used to keep particles inside the search space. We compare here nine methods on a few benchmark functions, and the results suggest another one which is less biased. Furthermore this study also suggests how to adaptively modify the...

The work proposes the application of neural networks with particle swarm optimisation (PSO) and genetic algorithms (GA) to compensate for missing data in classifying high voltage bushings. The classification is done using DGA data from 60966 bushings based on IEEEc57.104, IEC599 and IEEE production rates methods for oil impregnated paper (OIP) bushings. PSO and GA were compared in terms of accu...

The Particle Swarm Optimisation (PSO) algorithm has been established as a useful global optimisation algorithm for multi-dimensional search spaces. A practical example is its success in training feed-forward neural networks. Such successes, however, must be judged relative to the complexity of the search space. In this paper we show that the effectiveness of the PSO algorithm breaks down when e...

In this work, a novel approach of merging two swarm intelligence algorithms is considered – one mimicking the behaviour of ants foraging (Stochastic Diffusion Search [5]) and the other algorithm simulating the behaviour of birds flocking (Particle Swarm Optimisation [17]). This hybrid algorithm is assisted by a mechanism inspired from the behaviour of skeletal muscles activated by motor neurons...

This paper proposes a method for designing EMC shielding enclosures using a peer-to-peer based distributed optimisation system based on a modified particle swarm optimisation (PSO) algorithm. This optimisation system is used to efficiently obtain optimal solutions to a shielding enclosure design problem by sampling only a small fraction of the total problem space. Such a system would find use i...

In this paper, we address full-body articulated human motion tracking from multi-view video sequences acquired in a studio environment. The tracking is formulated as a multi-dimensional nonlinear optimisation and solved using particle swarm optimisation (PSO), a swarm-intelligence algorithm which has gained popularity in recent years due to its ability to solve difficult nonlinear optimisation ...