Applying Particle Swarm Optimization to Solve Portfolio Selection Problems

Particle swarm optimization (PSO), introduced by Kennedy and Eberhart in 1995, is a social population-based search algorithm and is generally similar to the evolutionary computation techniques that have been successfully applied to solve various hard optimization problems. The standard Markowitz mean-variance approach to portfolio selection involves tracing out an efficient frontier, a continuous curve illustrating the tradeoff between return and risk. In this paper we applied the particle swarm approach to find an efficient frontier associated with the classical and general (unconstrained and constrained) mean-variance portfolio selection problem. The OR library data sets were tested in our paper and computational results showed that the PSO found better solutions when compared to genetic algorithm (GA), simulated annealing(SA), and tabu search(TS)

Global numerical optimization with a bi-population particle swarm optimizer

This paper presents an enhanced Particle Swarm Optimizer approach, which is designed to solve numerical unconstrained optimization problems. The approach incorporates a dual population in an attempt to overcome the problem of premature convergence to local optima. The proposed algorithm is validated using standard test functions (unimodal, multi-modal, separable and nonseparable) taken from the specialized literature. The results are compared with values obtained by an algorithm representative of the state-of-the-art in the area. Our preliminary results indicate that our proposed approach is a competitive alternative to solve global optimization problems.Este artículo presenta un nuevo algoritmo Particle Swarm Optimizer, diseñado para resolver problemas de optimización numéricos sin restricciones, que incorpora una población dual para intentar solucionar el problema de convergencia prematura en óptimos locales. El algoritmo propuesto es validado usando funciones de prueba estandard (unimodales, multi-modales, separables y no separables) tomadas de la literatura especializada. Los resultados son comparados con los valores obtenidos por un algoritmo representativo del estado del arte en el área. Los resultados preliminares indican que la propuesta es una alternativa competitiva para resolver problemas de optimización global.VIII Workshop de Agentes y Sistemas InteligentesRed de Universidades con Carreras en Informática (RedUNCI

Imitating individualized facial expressions in a human-like avatar through a hybrid particle swarm optimization - tabu search algorithm

This thesis describes a machine learning method for automatically imitating a particular person\u27s facial expressions in a human-like avatar through a hybrid Particle Swarm Optimization - Tabu Search algorithm. The muscular structures of the facial expressions are measured by Ekman and Friesen\u27s Facial Action Coding System (FACS). Using a neutral face as a reference, the minute movements of the Action Units, used in FACS, are automatically tracked and mapped onto the avatar using a hybrid method. The hybrid algorithm is composed of Kennedy and Eberhart\u27s Particle Swarm Optimization algorithm (PSO) and Glover\u27s Tabu Search (TS). Distinguishable features portrayed on the avatar ensure a personalized, realistic imitation of the facial expressions. To evaluate the feasibility of using PSO-TS in this approach, a fundamental proof-of-concept test is employed on the system using the OGRE avatar. This method is analyzed in-depth to ensure its proper functionality and evaluate its performance compared to previous work

Anales del XIII Congreso Argentino de Ciencias de la Computación (CACIC)

Contenido: Arquitecturas de computadoras Sistemas embebidos Arquitecturas orientadas a servicios (SOA) Redes de comunicaciones Redes heterogéneas Redes de Avanzada Redes inalámbricas Redes móviles Redes activas Administración y monitoreo de redes y servicios Calidad de Servicio (QoS, SLAs) Seguridad informática y autenticación, privacidad Infraestructura para firma digital y certificados digitales Análisis y detección de vulnerabilidades Sistemas operativos Sistemas P2P Middleware Infraestructura para grid Servicios de integración (Web Services o .Net)Red de Universidades con Carreras en Informática (RedUNCI

Evolving the Structure of the Particle Swarm Optimization Algorithms

A new model for evolving the structure of a Particle Swarm Optimization (PSO) algorithm is proposed in this paper. The model is a hybrid technique that combines a Genetic Algorithm (GA) and a PSO algorithm. Each GA chromosome is an array encoding a meaning for updating the particles of the PSO algorithm. The evolved PSO algorithm is compared to a human-designed PSO algorithm by using ten artificially constructed functions and one real-world problem. Numerical experiments show that the evolved PSO algorithm performs similarly and sometimes even better than standard approaches for the considered problems