Evolutionary Computation 2020

Intelligent optimization is based on the mechanism of computational intelligence to refine a suitable feature model, design an effective optimization algorithm, and then to obtain an optimal or satisfactory solution to a complex problem. Intelligent algorithms are key tools to ensure global optimization quality, fast optimization efficiency and robust optimization performance. Intelligent optimization algorithms have been studied by many researchers, leading to improvements in the performance of algorithms such as the evolutionary algorithm, whale optimization algorithm, differential evolution algorithm, and particle swarm optimization. Studies in this arena have also resulted in breakthroughs in solving complex problems including the green shop scheduling problem, the severe nonlinear problem in one-dimensional geodesic electromagnetic inversion, error and bug finding problem in software, the 0-1 backpack problem, traveler problem, and logistics distribution center siting problem. The editors are confident that this book can open a new avenue for further improvement and discoveries in the area of intelligent algorithms. The book is a valuable resource for researchers interested in understanding the principles and design of intelligent algorithms

On a Nonsmooth Gauss–Newton Algorithms for Solving Nonlinear Complementarity Problems

In this paper, we propose a new version of the generalized damped Gauss–Newton method for solving nonlinear complementarity problems based on the transformation to the nonsmooth equation, which is equivalent to some unconstrained optimization problem. The B-differential plays the role of the derivative. We present two types of algorithms (usual and inexact), which have superlinear and global convergence for semismooth cases. These results can be applied to efficiently find all solutions of the nonlinear complementarity problems under some mild assumptions. The results of the numerical tests are attached as a complement of the theoretical considerations

Development of safety improvement method in city zones based on road network characteristics

Background and Objective: Extensive studies have so far been carried out on developing safety models. Despite the extensive efforts made in identifying independent variables and methods for developing models, little research has been carried out in providing amendatory solutions for enhancing the level of safety. Thus, the present study first developed separate accident frequency prediction models by transportation modes, and then in the second phase, a development of safety improvement method (DSIM) was proposed. Materials and Methods: To this end, the data related to 14,903 accidents in 96 traffic analysis zones in Tehran, Iran, were collected. To evaluate the effect of intra-zone correlation, a multilevel model and a negative binomial (NB) model were developed based on both micro- and macro-level independent variables. Next, the DSIM was provided, aiming at causing the least change in the area under study and with attention to the defined constraints and ideal gas molecular movement algorithm. Results: Based on a comparison of the goodness-of-fit measures for the multilevel model with those of the NB model, the multilevel models showed a better performance in explaining the factors affecting accidents. This is due to considering the multilevel structure of the data in such models. The final results were obtained after 200 iterations of the optimization algorithm. Thus, to decrease accidents by 30 and cause the least change in the area under study, the independent variable of vehicle kilometer traveled per road segment underwent a considerable change, while little change was observed for the other variables. Conclusions: The final results of the DSIM showed that the ultimate solutions derived from this method can be different from the final solutions derived from the analysis of the results from the safety models. Hence, it is necessary to develop new methods to propose solutions for increasing safety

Comprehensive Taxonomies of Nature- and Bio-inspired Optimization: Inspiration versus Algorithmic Behavior, Critical Analysis and Recommendations

In recent years, a great variety of nature- and bio-inspired algorithms has been reported in the literature. This algorithmic family simulates different biological processes observed in Nature in order to efficiently address complex optimization problems. In the last years the number of bio-inspired optimization approaches in literature has grown considerably, reaching unprecedented levels that dark the future prospects of this field of research. This paper addresses this problem by proposing two comprehensive, principle-based taxonomies that allow researchers to organize existing and future algorithmic developments into well-defined categories, considering two different criteria: the source of inspiration and the behavior of each algorithm. Using these taxonomies we review more than three hundred publications dealing with nature-inspired and bio-inspired algorithms, and proposals falling within each of these categories are examined, leading to a critical summary of design trends and similarities between them, and the identification of the most similar classical algorithm for each reviewed paper. From our analysis we conclude that a poor relationship is often found between the natural inspiration of an algorithm and its behavior. Furthermore, similarities in terms of behavior between different algorithms are greater than what is claimed in their public disclosure: specifically, we show that more than one-third of the reviewed bio-inspired solvers are versions of classical algorithms. Grounded on the conclusions of our critical analysis, we give several recommendations and points of improvement for better methodological practices in this active and growing research field.Comment: 76 pages, 6 figure

Bio-inspired optimization in integrated river basin management

Water resources worldwide are facing severe challenges in terms of quality and quantity. It is essential to conserve, manage, and optimize water resources and their quality through integrated water resources management (IWRM). IWRM is an interdisciplinary field that works on multiple levels to maximize the socio-economic and ecological benefits of water resources. Since this is directly influenced by the river’s ecological health, the point of interest should start at the basin-level. The main objective of this study is to evaluate the application of bio-inspired optimization techniques in integrated river basin management (IRBM). This study demonstrates the application of versatile, flexible and yet simple metaheuristic bio-inspired algorithms in IRBM. In a novel approach, bio-inspired optimization algorithms Ant Colony Optimization (ACO) and Particle Swarm Optimization (PSO) are used to spatially distribute mitigation measures within a basin to reduce long-term annual mean total nitrogen (TN) concentration at the outlet of the basin. The Upper Fuhse river basin developed in the hydrological model, Hydrological Predictions for the Environment (HYPE), is used as a case study. ACO and PSO are coupled with the HYPE model to distribute a set of measures and compute the resulting TN reduction. The algorithms spatially distribute nine crop and subbasin-level mitigation measures under four categories. Both algorithms can successfully yield a discrete combination of measures to reduce long-term annual mean TN concentration. They achieved an 18.65% reduction, and their performance was on par with each other. This study has established the applicability of these bio-inspired optimization algorithms in successfully distributing the TN mitigation measures within the river basin. Stakeholder involvement is a crucial aspect of IRBM. It ensures that researchers and policymakers are aware of the ground reality through large amounts of information collected from the stakeholder. Including stakeholders in policy planning and decision-making legitimizes the decisions and eases their implementation. Therefore, a socio-hydrological framework is developed and tested in the Larqui river basin, Chile, based on a field survey to explore the conditions under which the farmers would implement or extend the width of vegetative filter strips (VFS) to prevent soil erosion. The framework consists of a behavioral, social model (extended Theory of Planned Behavior, TPB) and an agent-based model (developed in NetLogo) coupled with the results from the vegetative filter model (Vegetative Filter Strip Modeling System, VFSMOD-W). The results showed that the ABM corroborates with the survey results and the farmers are willing to extend the width of VFS as long as their utility stays positive. This framework can be used to develop tailor-made policies for river basins based on the conditions of the river basins and the stakeholders' requirements to motivate them to adopt sustainable practices. It is vital to assess whether the proposed management plans achieve the expected results for the river basin and if the stakeholders will accept and implement them. The assessment via simulation tools ensures effective implementation and realization of the target stipulated by the decision-makers. In this regard, this dissertation introduces the application of bio-inspired optimization techniques in the field of IRBM. The successful discrete combinatorial optimization in terms of the spatial distribution of mitigation measures by ACO and PSO and the novel socio-hydrological framework using ABM prove the forte and diverse applicability of bio-inspired optimization algorithms

Advances in Artificial Intelligence: Models, Optimization, and Machine Learning

The present book contains all the articles accepted and published in the Special Issue “Advances in Artificial Intelligence: Models, Optimization, and Machine Learning” of the MDPI Mathematics journal, which covers a wide range of topics connected to the theory and applications of artificial intelligence and its subfields. These topics include, among others, deep learning and classic machine learning algorithms, neural modelling, architectures and learning algorithms, biologically inspired optimization algorithms, algorithms for autonomous driving, probabilistic models and Bayesian reasoning, intelligent agents and multiagent systems. We hope that the scientific results presented in this book will serve as valuable sources of documentation and inspiration for anyone willing to pursue research in artificial intelligence, machine learning and their widespread applications

Swarm intelligence algorithms adaptation for various search spaces

U današnje vrijeme postoji mnogo algoritama inteligencije rojeva koji se uspiješno koriste za rešavanje raznih teških problema optimizacije. Zajednicki elementi svih ovih algoritama su operator za lokalnu pretragu (eksploataciju) oko prona enih obecavajucih rješenja i operator globalne pretrage (eksploracije) koji pomaže u bijegu iz lokalnih optimuma. Algoritmi inteligencije rojeva obicno se inicijalno testiraju na neogranicenim, ogranicenim ili visoko-dimenzionalnim skupovima standardnih test funkcija. Nadalje, mogu se poboljšati, prilagoditi, izmijeniti, hibridizirati, kombinirati s lokalnom pretragom. Konacna svrha je korištenje takve metaheuristike za optimizaciju problema iz stvarnog svijeta. Domeni rješenja odnosno prostori pretrage prakticnih teških problema optimizacije mogu biti razliciti. Rješenja mogu biti vektori iz skupa realnih brojeva, cijelih brojeva ali mogu biti i kompleksnije strukture. Algoritmi inteligencije rojeva moraju se prilagoditi za razlicite prostore pretrage što može biti jednostavno podešavanje parametera algoritma ili prilagodba za cjelobrojna rješenja jednostavnim zaokruživanjem dobivenih realnih rješenja ali za pojedine prostore pretrage potrebnao je skoro kompletno prepravljanja algoritma ukljucujuci i operatore ekploatacije i ekploracije zadržavajuci samo proces vo enja odnosno inteligenciju roja. U disertaciji je predstavljeno nekoliko algoritama inteligencije rojeva i njihova prilagodba za razlicite prostore pretrage i primjena na prakticne probleme. Ova disertacija ima za cilj analizirati i prilagoditi, u zavisnosti od funkcije cilja i prostora rješenja, algoritme inteligencije rojeva. Predmet disertacije ukljucuje sveobuhvatan pregled postojecih implementacija algoritama inteligencije rojeva. Disertacija tako er obuhvaca komparativnu analizu, prikaz slabosti i snaga jednih algoritama u odnosu na druge zajedno s istraživanjem prilagodbi algoritama inteligencije rojeva za razlicite prostore pretrage i njihova primjena na prakticne problem. Razmatrani su problemi sa realnim rješenjima kao što su optimizacija stroja potpornih vektora, grupiranje podataka, sa cijelobrojnim rješenjima kao što je slucaj problema segmentacije digitalnih slika i za probleme gdje su rješenja posebne strukture kao što su problemi planiranja putanje robota i triangulacije minimalne težine. Modificirani i prilago eni algoritmi inteligencije rojeva za razlicite prostore pretrage i primjenih na prakticne probleme testirani su na standardnim skupovima test podataka i uspore eni s drugim suvremenim metodama za rješavanje promatranih problema iz literature. Pokazane su uspješne prilagodbe algoritama inteligencije rojeva za razne prostore pretrage. Ovako prilago eni algoritmi su u svim slucajevima postigli bolje rezultate u usporedbi sa metodama iz literature, što dovodi do zakljucka da je moguce prilagoditi algoritme inteligencije rojeva za razne prostore pretrage ukljucujuci i kompleksne strukture i postici bolje rezultate u usporedbi sa metodama iz literature

Etude de l’é quilibre entre phasesliquides des systè mes é lectrolytes etnon é lectrolytes : Expé rimentation etmodé lisation.

L es données d'équilibre liquide-liquide (ELL)pour le systèm e partiellem ent m iscible (Eau + Butanone)ont été m esurées enprésences de NaCl,KCl etLiCl à 25 et 30°C.L es systèm es ont été com parés enterm es d'efficacité de salting-out etd'effets de solvatation.L es données (ELL)pourles systèm es(Eau + 1-Pentanol + DMAC) et(Eau + 1-Hexanol + DMAC)ontété déterm inéesaussià 25 et 35°C sous 101,1 kPa,les diagram m es de phases pour ces systèm es sont de type I, selonla classificationTrayball. L a linéarité de l'équationde Setschenow indique que l'influence desselsutiliséssurlesdonnées (ELL)dansle systèm e binaire étudié estadditive.A lors,que l’équilibre enprésence de sel,dim inuent avec la tem pérature. L a cohérence therm odynam ique des données m esurées pour les systèm es ternaires a été vérifiée pardifférentes équations de corrélation.L es rapports de distribution(D)etles valeurs de sélectivité (S)ontété calculés enfonctiondes données d’équilibre m esurées pourévaluer lescom portem entsd'extractiondessolvantssélectionnés. L e m odèle prédictifUNIQUAC étendu-m odifié etle réseau de neuronesartificiels(ANN)ontété utilisés pour résoudre le com portem ent de phase de ELL des systèm es binaires. L a com paraisonde deuxm odèlesindique que lescapacitésde prédictiondu m odèle ANN sontbienm eilleuresparrapport au m odèle therm odynam ique.D 'autre part,les données expérim entales des systèm es ternairesontété corrélées par les équations du coefficient d'activité nonaléatoire à deuxliquides (NRTL)et quasi chim ique universel (UNIQUAC)avec l'algorithm e d'optim isationde troupeau d'éléphants (EHA). L'EHA est une nouvelle m éthode d'optim isationglobale stochastique inspirée de la nature et a été utilisée pourl'estim ationdesparam ètresd'ELL des m odèlesde com positionlocale dansdesm élanges m ulti- com posants.L esperform ances de cette nouvelle m éthode de recherche m éta-heuristique basée surlesessaim spourla m odélisationELL avec etsansl'équationde ferm eture ontété analysées

Feature Selection for Document Classification : Case Study of Meta-heuristic Intelligence and Traditional Approaches

Doctor of Philosophy (Computer Engineering), 2020Nowadays, the culture for accessing news around the world is changed from paper to electronic format and the rate of publication for newspapers and magazines on website are increased dramatically. Meanwhile, text feature selection for the automatic document classification (ADC) is becoming a big challenge because of the unstructured nature of text feature, which is called “multi-dimension feature problem”. On the other hand, various powerful schemes dealing with text feature selection are being developed continuously nowadays, but there still exists a research gap for “optimization of feature selection problem (OFSP)”, which can be looked for the global optimal features. Meanwhile, the capacity of meta-heuristic intelligence for knowledge discovery process (KDP) is also become the critical role to overcome NP-hard problem of OFSP by providing effective performance and efficient computation time. Therefore, the idea of meta-heuristic based approach for optimization of feature selection is proposed in this research to search the global optimal features for ADC. In this thesis, case study of meta-heuristic intelligence and traditional approaches for feature selection optimization process in document classification is observed. It includes eleven meta-heuristic algorithms such as Ant Colony search, Artificial Bee Colony search, Bat search, Cuckoo search, Evolutionary search, Elephant search, Firefly search, Flower search, Genetic search, Rhinoceros search, and Wolf search, for searching the optimal feature subset for document classification. Then, the results of proposed model are compared with three traditional search algorithms like Best First search (BFS), Greedy Stepwise (GS), and Ranker search (RS). In addition, the framework of data mining is applied. It involves data preprocessing, feature engineering, building learning model and evaluating the performance of proposed meta-heuristic intelligence-based feature selection using various performance and computation complexity evaluation schemes. In data processing, tokenization, stop-words handling, stemming and lemmatizing, and normalization are applied. In feature engineering process, n-gram TF-IDF feature extraction is used for implementing feature vector and both filter and wrapper approach are applied for observing different cases. In addition, three different classifiers like J48, Naïve Bayes, and Support Vector Machine, are used for building the document classification model. According to the results, the proposed system can reduce the number of selected features dramatically that can deteriorate learning model performance. In addition, the selected global subset features can yield better performance than traditional search according to single objective function of proposed model