The use of Particle Swarm Optimization, a heuristic optimization technique based on the concept of swarm, is described to face the problem of classification of instances in multiclass databases. Three different fitness functions are taken into account, resulting in three versions being investigated. Their performance is contrasted on 13 typical test databases. The resulting best version is then compared against other nine classification techniques well known in literature. Results show the competitiveness of Particle Swarm Optimization. In particular, it turns out to be the best on 3 out of the 13 challenged problems
The aim of this paper is to introduce a methodology based on the particle swarm optimization (PSO) a...
We describe the performance of two population based search algorithms (genetic algorithms and partic...
Optimization problems are classified into continuous, discrete, constrained, unconstrained determini...
The use of Particle Swarm Optimization, a heuristic optimization technique based on the concept of s...
The use of Particle Swarm Optimization, a heuristic optimization technique based on the concept of s...
The aim of this research is to register satellite images on the DSP processor using probabilistic op...
A new multi-sensor image registration technique is proposed based on detecting the feature corner po...
Abstract. This paper describes the implementation of Data Mining tasks using Particle Swarm Optimise...
This paper focuses on optimisation algorithms inspired by swarm intelligence for satellite image cl...
<div><p>Many swarm optimization algorithms have been introduced since the early 60’s, Evolutionary P...
Data mining is the most commonly used name to solve problems by analyzing data already present in da...
Particle Swarm Optimisers are inherently distributed algorithms where the solution for a problem eme...
In recent years, the Particle Swarm Optimization has rapidly gained increasing popularity and many v...
We use evolutionary computation (EC) to automatically find problems which demonstrate the strength a...
Evolutionary algorithms (EAs) and swarm algorithms (SAs) have shown their usefulness in solving comb...
The aim of this paper is to introduce a methodology based on the particle swarm optimization (PSO) a...
We describe the performance of two population based search algorithms (genetic algorithms and partic...
Optimization problems are classified into continuous, discrete, constrained, unconstrained determini...
The use of Particle Swarm Optimization, a heuristic optimization technique based on the concept of s...
The use of Particle Swarm Optimization, a heuristic optimization technique based on the concept of s...
The aim of this research is to register satellite images on the DSP processor using probabilistic op...
A new multi-sensor image registration technique is proposed based on detecting the feature corner po...
Abstract. This paper describes the implementation of Data Mining tasks using Particle Swarm Optimise...
This paper focuses on optimisation algorithms inspired by swarm intelligence for satellite image cl...
<div><p>Many swarm optimization algorithms have been introduced since the early 60’s, Evolutionary P...
Data mining is the most commonly used name to solve problems by analyzing data already present in da...
Particle Swarm Optimisers are inherently distributed algorithms where the solution for a problem eme...
In recent years, the Particle Swarm Optimization has rapidly gained increasing popularity and many v...
We use evolutionary computation (EC) to automatically find problems which demonstrate the strength a...
Evolutionary algorithms (EAs) and swarm algorithms (SAs) have shown their usefulness in solving comb...
The aim of this paper is to introduce a methodology based on the particle swarm optimization (PSO) a...
We describe the performance of two population based search algorithms (genetic algorithms and partic...
Optimization problems are classified into continuous, discrete, constrained, unconstrained determini...