<abstract xmlns="http://www.w3.org/1999/xhtml"><p> Clustering optimization methods for continuous nu- merical multivariable functions have been used for increasing the eficiency in the selection of the start points in multi-start global optimization methods. Methods of this kind usually have three steps: (1) sampling points in the search domain, (2) transforming the sampled points in order to obtain points grouped in neigh- bourhoods of local optima, (3) using a clustering technique to identify the clusters. After the clusters are successfully identi- fied, the set of local optima (and from it the global optimum) can be easily determined by applying a local optimization method for each cluster. The novel Particle Swarm Clustering Optimization (PSCO) method proposed in this paper is concerned with simul- taneous integration of steps (1), (2) and (3) from the classical clustering optimization methods by applying Swarm Intelligence (SI) techniques. Two existing SI methods provided inspiration in the design of the PSCO method: Particle Swarm Optimization (PSO) and Firey Algorithm (FA). </p></abstract>

Clustering optimization methods for continuous nu- merical multivariable functions have been used for increasing the eficiency in the selection of the start points in multi-start global optimization methods. Methods of this kind usually have three steps: (1) sampling points in the search domain, (2) transforming the sampled points in order to obtain points grouped in neigh- bourhoods of local optima, (3) using a clustering technique to identify the clusters. After the clusters are successfully identi- fied, the set of local optima (and from it the global optimum) can be easily determined by applying a local optimization method for each cluster. The novel Particle Swarm Clustering Optimization (PSCO) method proposed in this paper is concerned with simul- taneous integration of steps (1), (2) and (3) from the classical clustering optimization methods by applying Swarm Intelligence (SI) techniques. Two existing SI methods provided inspiration in the design of the PSCO method: Particle Swarm Optimization (PSO) and Firey Algorithm (FA).

Particle Swarm Clustering Optimization - a novel Swarm Intelligence approach to Global Optimization

Power Plant Engineering Faculty Polytechnic University of Bucharest 313 plaiul Independentei, 060042 Bucharest Romania

Annals of West University of Timisoara - Mathematics and Computer Science

Clustering optimization methods for continuous nu- merical multivariable functions have been used for increasing the eficiency in the selection of the start...

Particle Swarm Clustering Optimization - a novel Swarm Intelligence approach to Global Optimization

Pubblicato online: 14 ago 2013

Pagine: 3 - 24

DOI: https://doi.org/10.2478/awutm-2013-0001

Parole chiave
Clustering Optimization, Swarm Intelligence (SI), Continuous Global Optimization Problem (CGOP) Particle Swarm Optimization (PSO), Firey Algorithm (FA), Fitness and Euclidean-distance Ratio (FER)

This content is open access.

Particle Swarm Clustering Optimization - a novel Swarm Intelligence approach to Global Optimization

Pubblicato online: 14 ago 2013

Pagine: 3 - 24

DOI: https://doi.org/10.2478/awutm-2013-0001

Parole chiaveClustering Optimization, Swarm Intelligence (SI), Continuous Global Optimization Problem (CGOP) Particle Swarm Optimization (PSO), Firey Algorithm (FA), Fitness and Euclidean-distance Ratio (FER)

This content is open access.

Parole chiave
Clustering Optimization, Swarm Intelligence (SI), Continuous Global Optimization Problem (CGOP) Particle Swarm Optimization (PSO), Firey Algorithm (FA), Fitness and Euclidean-distance Ratio (FER)