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

Reconsiderando o efeito Fisher: uma análise de cointegração entre taxas de juros e inflação [Rethinking the Fisher Effect: a co-integration analysis between interest rates and inflation]

This paper investigates the validity of the Fisher effect hypothesis that it is the interest rate which moves to adjust to the anticipated changes in the rate of inflation. The analysis is carried out with monthly data for the period 1980-97 for three countries with recent histories of chronic high inflation: Argentina, Brazil, and Mexico. A co-integration analysis has provided evidence of a stable long-run equilibrium relationship between nominal interest rates and the inflation rate for the cases of Argentina and Brazil only.inflation, interest rate, monetary policy

Heuristic-based firefly algorithm for bound constrained nonlinear binary optimization

Firefly algorithm (FA) is a metaheuristic for global optimization. In this paper,we address the practical testing of aheuristic-based FA (HBFA) for computing optimaof discrete nonlinear optimization problems,where the discrete variables are of binary type. An important issue in FA is the formulation of attractiveness of each firefly which in turn affects its movement in the search space. Dynamic updating schemes are proposed for two parameters, one from the attractiveness term and the other from the randomization term. Three simple heuristics capable of transforming real continuous variables into binary ones are analyzed. A new sigmoid ‘erf’ function is proposed. In the context of FA, three different implementations to incorporate the heuristics for binary variables into the algorithm are proposed. Based on a set of benchmark problems, a comparison is carried out with other binary dealing metaheuristics. The results demonstrate that the proposed HBFA is efficient and outperforms binary versions of differential evolution (DE) and particle swarm optimization (PSO). The HBFA also compares very favorably with angle modulated version of DE and PSO. It is shown that the variant of HBFA based on the sigmoid ‘erf’ function with ‘movements in continuous space’ is the best, both in terms of computational requirements and accuracy.Fundação para a Ciência e a Tecnologia (FCT

Extension of the firefly algorithm and preference rules for solving MINLP problems

An extension of the firefly algorithm (FA) for solving mixed-integer nonlinear programming (MINLP) problems is presented. Although penalty functions are nowadays frequently used to handle integrality conditions and inequality and equality constraints, this paper proposes the implementation within the FA of a simple rounded-based heuristic and four preference rules to find and converge to MINLP feasible solutions. Preliminary numerical experiments are carried out to validate the proposed methodology.This work has been supported by COMPETE: POCI-01-0145-FEDER-007043 and FCT - Fundac¸ao para a Ci ˜ encia e Tecnologia, ˆ within the projects UID/CEC/00319/2013 and UID/MAT/00013/2013.info:eu-repo/semantics/publishedVersio

Comparison of penalty functions on a penalty approach to mixed-integer optimization

In this paper, we present a comparative study involving several penalty functions that can be used in a penalty approach for globally solving bound mixed-integer nonlinear programming (bMIMLP) problems. The penalty approach relies on a continuous reformulation of the bMINLP problem by adding a particular penalty term to the objective function. A penalty function based on the ‘erf’ function is proposed. The continuous nonlinear optimization problems are sequentially solved by the population-based firefly algorithm. Preliminary numerical experiments are carried out in order to analyze the quality of the produced solutions, when compared with other penalty functions available in the literature.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-UID/CEC/00319/2013

Polysaccharides As Viscosupplementation Agents: Structural Molecular Characteristics but Not Rheology Appear Crucial to the Therapeutic Response

IntroductionMost clinical studies and basic research document viscosupplementation (VS) in terms of effectiveness and safety, but only a few highlight its molecular mechanisms of action. Besides, there is generally focus on hyaluronic acid (HA) as being the most relevant polysaccharide to reach the clinical endpoints, attributing its effect mainly to its unique viscoelastic properties, related to a high-molecular weight and gel formulation. Usually, studies do not approach the possible biological pathways where HA may interfere, and there is a lack of reports on other biocompatible polysaccharides that could be of use in VS.AimWe briefly review the main proposed mechanisms of action of intra-articular hyaluronic acid (IA-HA) treatment and discuss its effectiveness focusing on the role of rheological and intrinsic structural molecular properties of polysaccharides in providing a therapeutic effect.MethodsWe conducted a literature search using PubMed database to find articles dealing with the mechanisms of action of IA-HA treatment and/or emphasizing how the structural properties of the polysaccharide used influenced the clinical outcomes.Discussion/conclusionHA is involved in numerous biochemical interactions that may explain the clinical benefits of VS, most of them resulting from HA–cluster of differentiation 44 receptor interaction. There are other important aspects apart from the molecular size or the colloidal state of the IA-HA involved in VS efficiency that still need to be consolidated. Indeed, it seems that clinical response may be dependent on the intrinsic properties of the polysaccharide, regardless of being HA, rather than to rheology, posing some controversy to previous beliefs

Compression of Surface Electromyographic Signals Using Two-Dimensional Techniques

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