Parameter selection in particle swarm optimisation: a survey

Parameter selection in particle swarm optimisation: a survey

A New Binary Particle Swarm Optimisation Algorithm for Feature Selection

Feature selection aims to select a small number of features from a large feature set to achieve similar or better classification performance than using all features. This paper develops a new binary particle swarm optimisation (PSO) algorithm (named PBPSO) based on which a new feature selection approach (PBPSOfs) is developed to reduce the number of features and increase the classification accu...

Finding the Best Parameter Setting Particle Swarm Optimisation

Information Retrieval techniques traditionally depend on the setting of one or more parameters. Depending on the problem and the techniques the number of parameters can be one, two or even dozens of them. One crucial problem in Information Retrieval research is to achieve a good parameter setting of its methods. The tuning process, when dealing with several parameters, is a time consuming and c...

Clustering-Based Leaders' Selection in Multi-Objective Particle Swarm Optimisation

Clustering-based Leaders’ Selection (CLS) is a novel approach for leaders selection in multi-objective particle swarm optimisation. Both objective and solution spaces are clustered. An indirect mapping between clusters in both spaces is defined to recognize regions with potentially better solutions. A leaders archive is built which contains representative particles of selected clusters in the o...

Geometric Particle Swarm Optimisation

Using a geometric framework for the interpretation of crossover of recent introduction, we show an intimate connection between particle swarm optimization (PSO) and evolutionary algorithms. This connection enables us to generalize PSO to virtually any solution representation in a natural and straightforward way. We demonstrate this for the cases of Euclidean, Manhattan and Hamming spaces.