Numerical integration of the incrementally non-linear, zero elastic range, bounding surface plasticity model for sand

SANISAND-Z is a recently developed plasticity model for sands with zero purely elastic range in stress space within the framework of Bounding Surface (BS) plasticity. As a consequence of zero elastic range the plastic strain increment direction, and consequently the elastic-plastic moduli fourth order tensor depends on the direction of the stress increment, rendering the model incrementally non-linear and intrinsically implicit. An iterative algorithm based on the Backward Euler method is presented to solve the non-linear system of ordinary differential equations. A non-traditional consistency condition based on the plastic multiplier is introduced as a core element of the system. A thorough analysis of the stability and accuracy of the algorithm is presented based on error estimation. The proposed integration scheme allows the use of SANISAND-Z framework in Finite Element Analysis

Incident detection using data from social media

This is an accepted manuscript of an article published by IEEE in 2017 IEEE 20th International Conference on Intelligent Transportation Systems (ITSC) on 15/03/2018, available online: https://ieeexplore.ieee.org/document/8317967/citations#citations The accepted version of the publication may differ from the final published version.© 2017 IEEE. Due to the rapid growth of population in the last 20 years, an increased number of instances of heavy recurrent traffic congestion has been observed in cities around the world. This rise in traffic has led to greater numbers of traffic incidents and subsequent growth of non-recurrent congestion. Existing incident detection techniques are limited to the use of sensors in the transportation network. In this paper, we analyze the potential of Twitter for supporting real-time incident detection in the United Kingdom (UK). We present a methodology for retrieving, processing, and classifying public tweets by combining Natural Language Processing (NLP) techniques with a Support Vector Machine algorithm (SVM) for text classification. Our approach can detect traffic related tweets with an accuracy of 88.27%.Published versio

A particle swarm optimization based memetic algorithm for dynamic optimization problems

Copyright @ Springer Science + Business Media B.V. 2010.Recently, there has been an increasing concern from the evolutionary computation community on dynamic optimization problems since many real-world optimization problems are dynamic. This paper investigates a particle swarm optimization (PSO) based memetic algorithm that hybridizes PSO with a local search technique for dynamic optimization problems. Within the framework of the proposed algorithm, a local version of PSO with a ring-shape topology structure is used as the global search operator and a fuzzy cognition local search method is proposed as the local search technique. In addition, a self-organized random immigrants scheme is extended into our proposed algorithm in order to further enhance its exploration capacity for new peaks in the search space. Experimental study over the moving peaks benchmark problem shows that the proposed PSO-based memetic algorithm is robust and adaptable in dynamic environments.This work was supported by the National Nature Science Foundation of China (NSFC) under Grant No. 70431003 and Grant No. 70671020, the National Innovation Research Community Science Foundation of China under Grant No. 60521003, the National Support Plan of China under Grant No. 2006BAH02A09 and the Ministry of Education, science, and Technology in Korea through the Second-Phase of Brain Korea 21 Project in 2009, the Engineering and Physical Sciences Research Council (EPSRC) of UK under Grant EP/E060722/01 and the Hong Kong Polytechnic University Research Grants under Grant G-YH60

Sibling relationships and family functioning in siblings of early adolescents, adolescents and young adults with autism spectrum disorder

The purpose of the study was to investigate how family functioning (defined as the ability that family members hold to manage stressful events, and intimate and social relationships), the degree to which family members feel happy and fulfilled with each other (called family satisfaction), and the demographical characteristics of siblings (age and gender) impacted on sibling relationships. The Circumplex Model of Marital and Family Systems and Behavioral Systems constituted the theoretical frameworks that guided our study. Eighty-six typically developing adolescents and young adults having a sister or a brother with autism spectrum disorder were enrolled. Results indicated that the youngest age group (early adolescents) reported to engage more frequently in negative behaviors with their siblings with ASD than the two older age groups (middle adolescents and young adults). No significant differences were found among the three age groups regarding behaviors derived from attachment, caregiving and affiliative systems. Family satisfaction and age significantly predicted behaviors during sibling interactions. Suggestions on prevention and intervention programs were discussed in order to prevent parentification among typically developing siblings and decrease episodes of quarrels and overt conflicts between brothers and sisters with and without AS

An artificial fish swarm filter-based Method for constrained global optimization

Ana Maria A.C. Rocha, M. Fernanda P. Costa and Edite M.G.P. Fernandes, An Artificial Fish Swarm Filter-Based Method for Constrained Global Optimization, B. Murgante, O. Gervasi, S. Mirsa, N. Nedjah, A.M. Rocha, D. Taniar, B. Apduhan (Eds.), Lecture Notes in Computer Science, Part III, LNCS 7335, pp. 57–71, Springer, Heidelberg, 2012.An artificial fish swarm algorithm based on a filter methodology for trial solutions acceptance is analyzed for general constrained global optimization problems. The new method uses 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. The preliminary numerical experiments with a wellknown benchmark set of engineering design problems show the effectiveness of the proposed method.Fundação para a Ciência e a Tecnologia (FCT

Multilocal programming and applications

Preprint versionMultilocal programming aims to identify all local minimizers of unconstrained or constrained nonlinear optimization problems. The multilocal programming theory relies on global optimization strategies combined with simple ideas that are inspired in deflection or stretching techniques to avoid convergence to the already detected local minimizers. The most used methods to solve this type of problems are based on stochastic procedures and a population of solutions. In general, population-based methods are computationally expensive but rather reliable in identifying all local solutions. In this chapter, a review on recent techniques for multilocal programming is presented. Some real-world multilocal programming problems based on chemical engineering process design applications are described.Fundação para a Ciência e a Tecnologia (FCT

Filter-based stochastic algorithm for global optimization

We propose the general Filter-based Stochastic Algorithm (FbSA) for the global optimization of nonconvex and nonsmooth constrained problems. Under certain conditions on the probability distributions that generate the sample points, almost sure convergence is proved. In order to optimize problems with computationally expensive black-box objective functions, we develop the FbSA-RBF algorithm based on the general FbSA and assisted by Radial Basis Function (RBF) surrogate models to approximate the objective function. At each iteration, the resulting algorithm constructs/updates a surrogate model of the objective function and generates trial points using a dynamic coordinate search strategy similar to the one used in the Dynamically Dimensioned Search method. To identify a promising best trial point, a non-dominance concept based on the values of the surrogate model and the constraint violation at the trial points is used. Theoretical results concerning the sufficient conditions for the almost surely convergence of the algorithm are presented. Preliminary numerical experiments show that the FbSA-RBF is competitive when compared with other known methods in the literature.The authors are grateful to the anonymous referees for their fruitful comments and suggestions.The first and second authors were partially supported by Brazilian Funds through CAPES andCNPq by Grants PDSE 99999.009400/2014-01 and 309303/2017-6. The research of the thirdand fourth authors were partially financed by Portuguese Funds through FCT (Fundação para Ciência e Tecnologia) within the Projects UIDB/00013/2020 and UIDP/00013/2020 of CMAT-UM and UIDB/00319/2020

Memetic and evolutionary algorithms to numerical optimization and to nonlinear dynamics

Τhe main topic of this thesis involved the development of new efficient Evolutionary Algorithms and their application on interesting scientific and engineering problems. There were two essential objectives. The first one was the investigation of further improvement on the performance of Evolutionary Algorithms by introducing new Memetic Algorithms. These are population-based heuristic search algorithms, designed to perform global optimization by combining evolutionary adaptation with individual learning within a lifetime. The second objective was the application of Evolutionary and Memetic Algorithms to problems of nonlinear dynamics. In the following paragraphs, the contributions of the thesis per objective are roughly described. Regarding the development of new Memetic Algorithms, approaches that combine Particle Swarm Optimization with local search methods were developed. Also, a criterion based on Shannon’s entropy was introduced in order to decide at which stages of the Memetic Algorithm the local search will be applied. All the new schemes were applied on a variety of optimization problems and compared with the standard Particle Swarm Optimization. Specifically, they were applied to global optimization problems with constraints, unconstrained optimization, integer and minimax programming. In the majority of these test problems, Memetic Algorithms significantly outperformed Particle Swarm Optimization or had statistically equivalent behavior in terms of the required number of function evaluations to reach the error-goal, while they exhibited also a higher number of successful experiments, implying their robustness. The proposed Memetic Algorithms were also used to improve Fuzzy Cognitive Maps, which are simulation tools for modeling and studying complex systems. The learning procedure of Fuzzy Cognitive Maps can be reduced to a global optimization problem. Memetic Algorithms were applied for learning in 4 different models. These models have been used to model two control problems, one for the control of a chemical process in industry and another for a heat exchanger system, one medical problem about radiation therapy, and one for the study of pollution in an ecological industrial park. Also, in these problems, a new methodology was proposed regarding the design of Memetic Algorithms, and more specifically for the adaptive determination of the number of function evaluations allowed for local search. Memetic Algorithms were applied successfully in all the aforementioned problems, exhibiting superior performance than Particle Swarm Optimization in terms of function evaluations and percentage of successful experiments. Another interesting field of application involved problems of nonlinear dynamics. The determination of the stability region of conservative maps is a very important issue. A new methodology was proposed to numerically approximate the stability region of conservative maps using the Differential Evolution algorithm and the Smaller ALignment Index method, which is used for the detection of chaotic orbits. Also the correlation dimension of the produced regions was computed to provide an estimation for the dimension of the studied maps. The conservative maps used to test the validity of the new methodology were the 2 and 4-dimensional maps of Hénon, which have been used to model the particles motion inside the particle accelerator’s machines. Furthermore, a new methodology that exploits the aforementioned approach was proposed to find frequencies that result in the map with the biggest possible region of stability. Very interesting results were obtained by both methodologies. The first methodology estimated with satisfactory accuracy the last invariant curve in the 2-dimensional case and the last invariant torus in the 4-dimensional case. The second methodology found a pair of frequencies, which resulted in a map with correlation dimension close to 3 and bounded orbits up to 108 iterations of the map. Further work was done on the existence of resonances in dynamical systems, which constitutes valuable information. The detection of resonances in conservative maps has been transformed to a global optimization problem and Differential Evolution was used to solve this problem. Since the existence of resonances is related with the integrability of a dynamical system, the proposed methodology can also serve as a numerical indicator for the integrability of a dynamical system. The proposed methodology has been applied to well known 2 and 4-dimensional maps, which are known to be chaotic or integrable, with very promising results. Specifically, it recognized resonances in cases where they existed, and in the case of integrable maps it gave no indication for the existence of resonances. The appealing feature of the proposed methodology is its applicability on conservative maps of any dimension. Finally, the problem of detecting periodic orbits was investigated. Periodic orbits comprise a very important tool for the study of dynamical systems, especially in cases of chaotic dynamical systems. Evolutionary and Memetic Algorithms were applied successfully for the detection of periodic orbits of nonlinear maps with high accuracy. These methods are applicable to nonlinear maps of any form, including discontinuous or non-differentiable maps. Also, these methods have performed very well in the case of chaotic maps where conventional methods failed. Memetic Algorithms exhibited better results than Evolutionary Algorithms on the problem of detecting periodic orbits, due to the usage of local search methods that have the ability to operate very effectively when they are in the basin of attraction of the desired optimum.Η παρούσα διατριβή έχει κύριο αντικείµενο µελέτης τους Εξελικτικούς Αλγόϱιθµους (ΕΑ). Ο εξελικτικός υπολογισµός ϐασίζεται στις θεωρίες της ϐιολογίας και συγκεκριµένα του ∆αρβίνου για την εξέλιξη των ειδών. Προσπαθεί να µιµηθεί τους µηχανισµούς που χρησιµοποιεί η ϕύση στα γονίδια των οργανισµών για την εξέλιξη τους. Τις τελευταίες δύο δεκαετίες, οι ΕΑ έχουν γνωρίσει σηµαντική αποδοχή από την επιστηµονική κοινότητα γιατί κατάφεραν να δώσουν λύσεις σε δύσκολα και πολύπλοκα προβλήµατα, στα οποία οι υπάρχουσες µεθοδολογίες και αλγόριθµοι αποτύγχαναν. Οι Μιµιδικοί Αλγόριθµοι (ΜΑ) είναι αλγόριθµοι εµπνευσµένοι από την πολιτισµική εξέλιξη παρά τη ϐιολογική. Μοιάζουν σε σηµαντικό ϐαθµό µε τους ΕΑ αλλά το κύριο χαρακτηριστικό τους είναι ότι υποστηρίζουν ότι το άτοµο κατά τη διάρκεια της ζωής του µπορεί να µάθει και να προσαρµοστεί στις υπάρχουσες συνθήκες, στοιχεία που µπορούν να επιταχύνουν την εξέλιξη και ικανότητα του. Στη ϐελτιστοποίηση, οι ΜΑ εµφανίζονται σαν υβριδικά σχήµατα τα οποία συνδυάζουν τους ΕΑ µε µεθόδους τοπικής αναζήτησης. Ένα µέρος της παρούσας διατριβής µελετάει τους ΜΑ και δείχνει ότι πράγµατι καταφέρνουν να ϐελτιώσουν την απόδοση των ΕΑ. Προτείνονται νέοι ΜΑ που συνδυάζουν τις µεθόδους Βελτιστοποίησης Σµήνους Σωµατιδίων (ΒΣΣ) και τον ∆ιαφοροεξελικτικό Αλγόριθµο (∆Α) µε ντετερµινιστικές και στοχαστικές µεθόδους αναζήτησης. Επίσης εξετάζονται αυτοί οι ΜΑ ως προς τη θεωρητική τους σύγκλιση. Από αυτούς τους συνδυασµούς προκύπτουν διάφορα σχήµατα τα οποία εφαρµόζονται σε µια πληθώρα προβληµάτων ολικής ϐελτιστοποίησης. Αυτά περιλαµβάνουν προβλήµατα ϐελτιστοποίησης χωρίς περιορισµούς, µε περιορισµούς, ακέραιου και ελαχιστοµέγιστου (minimax) προγραµµατισµού και το πρόβληµα µάθησης στις Ασαφείς Γνωστικές Απεικονίσεις. Τέλος, εξετάζεται η εφαρµογή Εξελικτικών και Μιµιδικών Αλγορίθµων σε θέµατα µη γραµµικών δυναµικών συστηµάτων. Η πολυπλοκότητα που εµφανίζεται στα συστήµατα αυτά είναι πολύ µεγάλη ακόµα και για συστήµατα µικρών διαστάσεων, οπότε η χρήση ευριστικών αλγορίθµων θα µπορούσε να δώσει λύσεις σε ζητήµατα τα οποία δεν µπορούν να αντιµετωπιστούν από αναλυτικές µεθόδους. Συγκεκριµένα, µελετώνται προβλήµατα σε διατηρητικές απεικονίσεις όπως η εκτίµηση της περιοχής ευστάθειας τους και η ανίχνευση συντονισµών σε αυτές. Μια επίσης σηµαντική εφαρµογή αποτελεί ο εντοπισµός περιοδικών τροχιών µεγάλης ακρίβειας σε µη γραµµικές απεικονίσεις