Leticia C. Cagnina

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This paper introduces a particle swarm optimization algorithm to solve constrained engineering optimization problems. The proposed approach uses a relatively simple method to handle constraints and a different mechanism to update the velocity and position of each particle. The algorithm is validated using four standard engineering design problems reported(More)
— This paper presents an enhanced Particle Swarm Optimizer approach, which is designed to solve numerical constrained optimization problems. The approach uses a single method to handle different types of constraints (linear, nonlinear, equality or inequality) and it incorporates a shake-mechanism and a dual population in an attempt to overcome the problem(More)
Nowadays, many decisions are based on information found in the Web. For the most part, the disseminating sources are not certified, and hence an assessment of the quality and credibility of Web content became more important than ever. With <i>factual density</i> we present a simple statistical quality measure that is based on facts extracted from Web(More)
This paper presents a particle swarm optimizer to solve constrained optimization problems. The proposed approach adopts a simple method to handle constraints of any type (linear, nonlinear, equality and inequality), and it also presents a novel mechanism to update the velocity and position of each particle. The approach is validated using standard test(More)
Work on " short-text clustering " is relevant, particularly if we consider the current/future mode for people to use 'small-language', e.g. blogs, text-messaging, snippets, etc. Potential applications in different areas of natural language processing may include re-ranking of snippets in information retrieval, and automatic clustering of scientific texts(More)
This paper presents a particle swarm optimization algorithm for solving general constrained optimization problems. The proposed approach introduces different methods to update the particle's information, as well as the use of a double population and a special shake mechanism designed to avoid premature convergence. It also incorporates a simple(More)
Clustering of short-text collections is a very relevant research area, given the current and future mode for people to use " small-language " (e.g. blogs, snippets , news and text-message generation such as email or chat). In recent years, a few approaches based on Particle Swarm Optimization (PSO) have been proposed to solve document clustering problems.(More)
This paper proposes a hybrid particle swarm approach called Simple Multi-Objective Particle Swarm Optimizer (SMOPSO) which incorporates Pareto dominance, an elitist policy, and two techniques to maintain diversity: a mutation operator and a grid which is used as a geographical location over objective function space. In order to validate our approach we use(More)