Extensions of firefly algorithm for nonsmooth nonconvex constrained optimization problems

Publicado em: "Computational science and its applications – ICCSA 2016: 16th International Conference, Beijing, China, July 4-7, 2016, Proceedings, Part I". ISBN 978-3-319-42084-4Firefly Algorithm (FA) is a stochastic population-based algorithm based on the flashing patterns and behavior of fireflies. Original FA was created and successfully applied to solve bound constrained optimization problems. In this paper we present extensions of FA for solving nonsmooth nonconvex constrained global optimization problems. To handle the constraints of the problem, feasibility and dominance rules and a fitness function based on the global competitive ranking, are proposed. To enhance the speed of convergence, the proposed extensions of FA invoke a stochastic local search procedure. Numerical experiments to validate the proposed approaches using a set of well know test problems are presented. The results show that the proposed extensions of FA compares favorably with other stochastic population-based methods.COMPETE: POCI-01-0145- FEDER-007043FCT – Fundação para a Ciência e Tecnologia within the projects UID/CEC/00319/2013 and UID/MAT/00013/201

A firefly dynamic penalty approach for solving engineering design problems

Firefly Algorithm is a recent swarm intelligence method, inspired by the social behavior of fireflies, based on their flashing and attraction characteristics [1, 2]. In this paper, we analyze the implementation of a dynamic penalty approach combined with the Firefly algorithm for solving constrained global optimization problems. In order to assess the applicability and performance of the proposed method, some benchmark problems from engineering design optimization are considered.The authors would like to thank the financial support from FCT (Fundação para a Ciência e Tecnologia, Portugal) in the scope of the projects: PEst-OE/MAT/UI0013/2014 and PEst-OE/EEI/UI0319/2014

A clustering algorithm based on fitness probability scores for cluster centers optimization

In the present paper, we propose an iterative clustering approach that sequentially applies five processes, namely: the assign, delete, split, delete and optimization. It is based on the fitness probability scores of the cluster centers to identify the least fitted centers to undergo an optimization process, aiming to improve the centers from one iteration to another. Moreover, the parameters of the algorithm for the delete, split and optimization processes are dynamically tuned as problem dependent functions. The presented clustering algorithm is evaluated using four data sets, two randomly generated and two well-known sets. The obtained clustering algorithm is compared with other clustering algorithms through the visualization of the clustering, the value of a validity measure and the value of the objective function of the optimization process. The comparison of results shows that the proposed clustering algorithm is effective and robust.This work has been supported by FCT -Fundacao para a Ciencia e Tecnologia within the R&D Units Project Scope: UIDB/00013/2020 and UIDP/00013/2020 of CMATUM

Los papeles y funciones del enfermero de salud laboral en Brasil y en los Estados Unidos de America

Cross-sectional exploratory research developed to outline the roles and functions of occupational health nurses (OHNs) in Brazil and compare them with those in the United States. The sample consisted of 154 Brazilian occupational health nurses. First, the instrument Job Analysis Survey of Occupational Health Nursing Practice was translated into Portuguese, followed by data collection and data analysis, comparing the results of the proposed study in Brazil with the findings of a job delineation study conducted in the United States. It was found that most were women, white, between 41 and 50 years of age and working primarily at hospitals or medical centers as clinicians and managers/administrators. Besides, it was found that most Brazilian OHNs spend more time in managerial roles, followed by consultant and educator responsibilities while, in the United States, OHNs spend significantly more time in education/advisory roles.Investigación transversal realizada con el objetivo de definir los papeles y funciones de los enfermeros de salud laboral (ESLs) en Brasil y compararles con los de los Estados Unidos de América (EUA). La muestra fue compuesta por ciento cincuenta y cuatro enfermeros de salud laboral brasileños. Primero, fue realizada la traducción de lo instrumento Job Analysis Survey of Occupational Health Nursing Practice para el Portugués, seguido de la recolecta y análisis de los datos, comparándolos con los hallazgos de un estudio conducido en los EUA. Se encontró que la mayoría eran mujeres, blancas, entre 41 y 50 años e trabajan en hospitales y centros médicos como enfermeros clínicos y administradores. También se encontró que ESLs brasileños dedican más tiempo en funciones administrativas, seguidas de responsabilidades de consultoría y educador, mientras en los EUA los ESLs dedican significativamente más tiempo en funciones educativas y de asesoría.Estudo transversal desenvolvido com o objetivo de delinear as responsabilidades e funções das enfermeiras do trabalho no Brasil e compará-las às atribuições das enfermeiras de saúde ocupacional nos Estados Unidos. A amostra foi composta por 154 enfermeiras do trabalho. Inicialmente, foi realizada a tradução para a língua portuguesa do instrumento de coleta de dados Job Analysis Survey of Occupational Health Nursing Practice, seguido da coleta e análise dos resultados, comparando-os aos dados obtidos num estudo realizado nos EUA. Foi constatado que a maioria das enfermeiras eram mulheres, brancas, com faixa etária entre 41 e 50 anos e trabalhavam em hospitais e centros médicos como enfermeiras clínicas ou administradoras. Além disso, observou-se que as enfermeiras brasileiras despendem maior tempo em funções administrativas, atividades de consultoria e ações educativas, enquanto que as enfermeiras norte-americanas gastam um tempo significativamente maior desenvolvendo papéis educativos e de consultoria

An artificial fish swarm algorithm based hyperbolic augmented Lagrangian method

This paper aims to present a hyperbolic augmented Lagrangian (HAL) framework with guaranteed convergence to an ϵ-global minimizer of a constrained nonlinear optimization problem. The bound constrained subproblems that emerge at each iteration k of the framework are solved by an improved artificial fish swarm algorithm. Convergence to an ϵk-global minimizer of the HAL function is guaranteed with probability one, where ϵk→ϵ as k→∞. Preliminary numerical experiments show that the proposed paradigm compares favorably with other penalty-type methods.Fundação para a Ciência e a Tecnologia (FCT

AFSFilter: artificial fish swarm filter-based algorithm for global optimization

A fish swarm intelligence algorithm based on the filter set concept to accept, at each iteration, a population of trial solutions whenever they improve constraint violation or objective function, relative to the current solutions, is proposed for constrained global continuous optimization problems. Preliminary numerical results are provided.Fundação para a Ciência e a Tecnologia (FCT

A penalty approach for solving nonsmooth and nonconvex MINLP problems

This paper presents a penalty approach for globally solving nonsmooth and nonconvex mixed-integer nonlinear programming (MINLP) problems. Both integrality constraints and general nonlinear constraints are handled separately by hyperbolic tangent penalty functions. Proximity from an iterate to a feasible promising solution is enforced by an oracle penalty term. The numerical experiments show that the proposed oracle-based penalty approach is effective in reaching the solutions of the MINLP problems and is competitive when compared with other strategies.FCT - Fundação para a Ciência e a Tecnologia.The authors would like to thank two anonymous referees for their valuable comments and suggestions to improve the paper. This work has been supported by COMPETE: POCI-01-0145-FEDER-007043 and FCT - Fundação para a Ciência e a Tecnologia within the projects UID/CEC/00319/2013 and UID/MAT/00013/ 2013.info:eu-repo/semantics/publishedVersio

On a hyperbolic augmented lagrangian artificial fish swarm based method: convergence issues

Fundação para a Ciência e a Tecnologia (FCT

Distribution based artificial fish swarm in continuous global optimization

Distribution based artificial fish swarm (DbAFS) is a new heuristic for continuous global optimization. Based on the artificial fish swarm paradigm, the new algorithm generates trial points from the Gaussian distribution, where the mean is the midpoint between the current and the target point and the standard deviation is the difference between those two points. A local search procedure is incorporated into the algorithm aiming to improve the quality of the solutions. The performance of the proposed DbAFS is investigated using a set of small bound constrained optimization problems.Fundação para a Ciência e a Tecnologia (FCT