A NOVEL THREE DEGREE-OF-FREEDOMS OSCILLATION SYSTEM OF INSECT FLAPPING WINGS

We propose an oscillation system to replicate the dynamic behavior of flapping wings, inspired by insect flight muscles. In particular, we study the flight of the fruit fly Drosophila virilis . We model the wing as a rigid body with three degree-of-freedom, described by three Euler angles: the stroke angle, the rotation angle and the deviation angle. Insect flight muscles are separated into two types: power muscles and control muscles. One actuator and one torsional spring at the stroke angle act as the power muscles. Two torsional springs at the rotation angle and the deviation angle mimic the control muscles. A dynamic model, using a blade-element model and a quasi-steady model to calculate aerodynamic forces and moments, is set up for analysis of the system\u27s performance. Using non-dimensional analysis, we are able to identify the dynamic behavior of the system through four coefficients: stroke stiffness coefficient, rotation stiffness coefficient, deviation stiffness coefficient and input torque coefficient. We use the dynamic model to explore a large coefficients space of the oscillation system. We find that tuning deviation stiffness coefficient and rotation stiffness coefficient generates four different types of wing trajectories. Among them, the one with a high deviation stiffness coefficient and a mediate rotation stiffness coefficient produces high lift and high power loading. Its wing trajectory is quite similar to the wing trajectory in actual insects. Furthermore, a hybrid optimization algorithm (a genetic algorithm and a Nelder-Mead simplex algorithm) is implemented to find the optimal stiffness coefficients. Through these coefficients, the system minimizes power loading while still providing enough lift to maintain a time-averaged constant altitude over one stroke cycle. The results of this optimization indicate that the flapping wing with nonzero deviation achieves a better aerodynamic performance than the wing with zero deviation. The oscillatory property of this system does not only explain how insects use flight muscles to tune wing kinematics, but it also allows for design simplifications of the wing driving mechanism of flapping micro air vehicles

Improvements on the bees algorithm for continuous optimisation problems

This work focuses on the improvements of the Bees Algorithm in order to enhance the algorithm’s performance especially in terms of convergence rate. For the first enhancement, a pseudo-gradient Bees Algorithm (PG-BA) compares the fitness as well as the position of previous and current bees so that the best bees in each patch are appropriately guided towards a better search direction after each consecutive cycle. This method eliminates the need to differentiate the objective function which is unlike the typical gradient search method. The improved algorithm is subjected to several numerical benchmark test functions as well as the training of neural network. The results from the experiments are then compared to the standard variant of the Bees Algorithm and other swarm intelligence procedures. The data analysis generally confirmed that the PG-BA is effective at speeding up the convergence time to optimum. Next, an approach to avoid the formation of overlapping patches is proposed. The Patch Overlap Avoidance Bees Algorithm (POA-BA) is designed to avoid redundancy in search area especially if the site is deemed unprofitable. This method is quite similar to Tabu Search (TS) with the POA-BA forbids the exact exploitation of previously visited solutions along with their corresponding neighbourhood. Patches are not allowed to intersect not just in the next generation but also in the current cycle. This reduces the number of patches materialise in the same peak (maximisation) or valley (minimisation) which ensures a thorough search of the problem landscape as bees are distributed around the scaled down area. The same benchmark problems as PG-BA were applied against this modified strategy to a reasonable success. Finally, the Bees Algorithm is revised to have the capability of locating all of the global optimum as well as the substantial local peaks in a single run. These multi-solutions of comparable fitness offers some alternatives for the decision makers to choose from. The patches are formed only if the bees are the fittest from different peaks by using a hill-valley mechanism in this so called Extended Bees Algorithm (EBA). This permits the maintenance of diversified solutions throughout the search process in addition to minimising the chances of getting trap. This version is proven beneficial when tested with numerous multimodal optimisation problems

Component-wise analysis of metaheuristic algorithms for novel fuzzy-meta classifier

Metaheuristic research has proposed promising results in science, business, and engineering problems. But, mostly high-level analysis is performed on metaheuristic performances. This leaves several critical questions unanswered due to black-box issue that does not reveal why certain metaheuristic algorithms performed better on some problems and not on others. To address the significant gap between theory and practice in metaheuristic research, this study proposed in-depth analysis approach using component-view of metaheuristic algorithms and diversity measurement for determining exploration and exploitation abilities. This research selected three commonly used swarm-based metaheuristic algorithms – Particle Swarm Optimization (PSO), Artificial Bee Colony (ABC), and Cuckoo Search (CS) – to perform component-wise analysis. As a result, the study able to address premature convergence problem in PSO, poor exploitation in ABC, and imbalanced exploration and exploitation issue in CS. The proposed improved PSO (iPSO), improved ABC (iABC), and improved CS (iCS) outperformed standard algorithms and variants from existing literature, as well as, Grey Wolf Optimization (GWO) and Animal Migration Optimization (AMO) on ten numerical optimization problems with varying modalities. The proposed iPSO, iABC, and iCS were then employed on proposed novel Fuzzy-Meta Classifier (FMC) which offered highly reduced model complexity and high accuracy as compared to Adaptive Neuro-Fuzzy Inference System (ANFIS). The proposed three-layer FMC produced efficient rules that generated nearly 100% accuracies on ten different classification datasets, with significantly reduced number of trainable parameters and number of nodes in the network architecture, as compared to ANFIS

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

A bumble bees mating optimization algorithm for global unconstrained optimization problems

Summarization: A new nature inspired algorithm, that simulates the mating behavior of the bumble bees, the Bumble Bees Mating Optimization (BBMO) algorithm, is presented in this paper for solving global unconstrained optimization problems. The performance of the algorithm is compared with other popular metaheuristic and nature inspired methods when applied to the most classic global unconstrained optimization problems. The methods used for comparisons are Genetic Algorithms, Island Genetic Algorithms, Differential Evolution, Particle Swarm Optimization, and the Honey Bees Mating Optimization algorithm. A high performance of the proposed algorithm is achieved based on the results obtained.Παρουσιάστηκε στο: Nature Inspired Cooperative Strategies for Optimization, Studies in Computational Intelligenc

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

Using MapReduce Streaming for Distributed Life Simulation on the Cloud

Distributed software simulations are indispensable in the study of large-scale life models but often require the use of technically complex lower-level distributed computing frameworks, such as MPI. We propose to overcome the complexity challenge by applying the emerging MapReduce (MR) model to distributed life simulations and by running such simulations on the cloud. Technically, we design optimized MR streaming algorithms for discrete and continuous versions of Conway’s life according to a general MR streaming pattern. We chose life because it is simple enough as a testbed for MR’s applicability to a-life simulations and general enough to make our results applicable to various lattice-based a-life models. We implement and empirically evaluate our algorithms’ performance on Amazon’s Elastic MR cloud. Our experiments demonstrate that a single MR optimization technique called strip partitioning can reduce the execution time of continuous life simulations by 64%. To the best of our knowledge, we are the first to propose and evaluate MR streaming algorithms for lattice-based simulations. Our algorithms can serve as prototypes in the development of novel MR simulation algorithms for large-scale lattice-based a-life models.https://digitalcommons.chapman.edu/scs_books/1014/thumbnail.jp

Diseño óptimo del refuerzo estructural, mediante disipadores CRP, para la adecuación del desempeño sísmico de estructuras aporticadas de hormigón armado

[ES] Se plantea el problema de determinar el diseño óptimo de la estructura de refuerzo conformada por disipadores de energía sísmica de tipo contraventeo restringido al pandeo (CRP), que permita el reajuste del desempeño sísmico de marcos de hormigón armado. Los CRP son elementos conformados por un núcleo metálico confinado, capaz de plastificar tanto a tracción como a compresión, característica que les permite disipar energía sísmica por medio de ciclos histeréticos. Debido a su comportamiento estable frente a acciones cíclicas, los disipadores de tipo CRP han sido utilizados tanto en el diseño como en el reajuste de estructuras desplantadas en zonas de elevado riesgo sísmico, como Japón, EU y América Latina. Debido al comportamiento no lineal que presenta la respuesta de estructuras equipadas con disipadores de tipo CRP, los diseños candidatos a solución fueron analizados por medio de OpenSees, el cual es un programa de elementos finitos enfocado a la ingeniería sísmica. El desempeño sísmico de todos los diseños fue evaluado por medio del método del espectro de capacidad, el cual utiliza la información de análisis estáticos no lineales para estimar el desplazamiento máximo que presentará la estructura bajo una demanda sísmica dada. El proceso de optimización se realizó por medio de algoritmo metaheurístico conocido como Simulated Annealing (SA), teniéndose como objetivo minimizar el material empleado en el núcleo de los disipadores. Las características geométricas del núcleo de los CRP¿s y su distribución en el marco fueron consideradas como variables, mientras que los elementos estructurales del marco se mantuvieron inalterados durante el proceso. El algoritmo de SA se corrió en el programa Matlab, creándose un vínculo entre este último y OpenSees para permitir la transferencia de información durante el proceso de optimización. Los resultados obtenidos indican que existen múltiples diseños de la estructura disipadora que permiten cumplir con los criterios límite de desempeño sísmico establecidos. De manera adicional, se observa que el perfil de rigidez lateral, aportada por el sistema disipador, no sigue una distribución lineal en altura, siendo una característica común la supresión del CRP en el último nivel.[EN] In this paper, we propose the problem of finding the optimum design of a reinforcement structure, made up buckling restrained braces (BRB), which allows retrofit the seismic performance of reinforced concrete frames. BRB¿s are elements formed by a confined steel core, capable of yielding both in tension and compression, characteristic that allows them to dissipate seismic energy by means of hysteretic cycles. Because of their stable behavior under cyclic actions, BRB's have been used in the design and retrofit of structures located in areas of high seismic risk, such as Japan, USA and Latin America. Due to the nonlinear behavior response of the structures equipped with BRB's, all candidate designs were analyzed using OpenSees, a finite element program focused on seismic engineering. The seismic performance of all designs was evaluated by the capacity spectrum method, which uses information from nonlinear static analyses to estimate the maximum displacement that the structure will present under a given seismic demand. The optimization process was carried out using the metaheuristic algorithm known as Simulated Annealing (SA), with the aim of minimizing the material used in the core of the braces. The geometrical characteristics of the BRB's core and its distribution in the frame were considered as decision variables, while frame's elements remained unchanged during the process. SA algorithm was run in Matlab, creating a link between this program and OpenSees to allow the share of information during the optimization process. The results obtained indicate that there are multiple designs of the dissipative structure that meet the established seismic performance limit criteria. Additionally, it is observed that the lateral stiffness profile, provided by the dissipative system, does not follow a linear distribution in height, being a common feature the suppression of the BRB's at the last level.Velasco Enriquez, LI. (2021). Diseño óptimo del refuerzo estructural, mediante disipadores CRP, para la adecuación del desempeño sísmico de estructuras aporticadas de hormigón armado. Universitat Politècnica de València. http://hdl.handle.net/10251/177909TFG

Approches générales de résolution pour les problèmes multi-attributs de tournées de véhicules et confection d'horaires

Thèse réalisée en cotutelle entre l'Université de Montréal et l'Université de Technologie de TroyesLe problème de tournées de véhicules (VRP) implique de planifier les itinéraires d'une flotte de véhicules afin de desservir un ensemble de clients à moindre coût. Ce problème d'optimisation combinatoire NP-difficile apparait dans de nombreux domaines d'application, notamment en logistique, télécommunications, robotique ou gestion de crise dans des contextes militaires et humanitaires. Ces applications amènent différents contraintes, objectifs et décisions supplémentaires ; des "attributs" qui viennent compléter les formulations classiques du problème. Les nombreux VRP Multi-Attributs (MAVRP) qui s'ensuivent sont le support d'une littérature considérable, mais qui manque de méthodes généralistes capables de traiter efficacement un éventail significatif de variantes. Par ailleurs, la résolution de problèmes "riches", combinant de nombreux attributs, pose d'importantes difficultés méthodologiques. Cette thèse contribue à relever ces défis par le biais d'analyses structurelles des problèmes, de développements de stratégies métaheuristiques, et de méthodes unifiées. Nous présentons tout d'abord une étude transversale des concepts à succès de 64 méta-heuristiques pour 15 MAVRP afin d'en cerner les "stratégies gagnantes". Puis, nous analysons les problèmes et algorithmes d'ajustement d'horaires en présence d'une séquence de tâches fixée, appelés problèmes de "timing". Ces méthodes, développées indépendamment dans différents domaines de recherche liés au transport, ordonnancement, allocation de ressource et même régression isotonique, sont unifiés dans une revue multidisciplinaire. Un algorithme génétique hybride efficace est ensuite proposé, combinant l'exploration large des méthodes évolutionnaires, les capacités d'amélioration agressive des métaheuristiques à voisinage, et une évaluation bi-critère des solutions considérant coût et contribution à la diversité de la population. Les meilleures solutions connues de la littérature sont retrouvées ou améliorées pour le VRP classique ainsi que des variantes avec multiples dépôts et périodes. La méthode est étendue aux VRP avec contraintes de fenêtres de temps, durée de route, et horaires de conducteurs. Ces applications mettent en jeu de nouvelles méthodes d'évaluation efficaces de contraintes temporelles relaxées, des phases de décomposition, et des recherches arborescentes pour l'insertion des pauses des conducteurs. Un algorithme de gestion implicite du placement des dépôts au cours de recherches locales, par programmation dynamique, est aussi proposé. Des études expérimentales approfondies démontrent la contribution notable des nouvelles stratégies au sein de plusieurs cadres méta-heuristiques. Afin de traiter la variété des attributs, un cadre de résolution heuristique modulaire est présenté ainsi qu'un algorithme génétique hybride unifié (UHGS). Les attributs sont gérés par des composants élémentaires adaptatifs. Des expérimentations sur 26 variantes du VRP et 39 groupes d'instances démontrent la performance remarquable de UHGS qui, avec une unique implémentation et paramétrage, égalise ou surpasse les nombreux algorithmes dédiés, issus de plus de 180 articles, révélant ainsi que la généralité ne s'obtient pas forcément aux dépends de l'efficacité pour cette classe de problèmes. Enfin, pour traiter les problèmes riches, UHGS est étendu au sein d'un cadre de résolution parallèle coopératif à base de décomposition, d'intégration de solutions partielles, et de recherche guidée. L'ensemble de ces travaux permet de jeter un nouveau regard sur les MAVRP et les problèmes de timing, leur résolution par des méthodes méta-heuristiques, ainsi que les méthodes généralistes pour l'optimisation combinatoire.The Vehicle Routing Problem (VRP) involves designing least cost delivery routes to service a geographically-dispersed set of customers while taking into account vehicle-capacity constraints. This NP-hard combinatorial optimization problem is linked with multiple applications in logistics, telecommunications, robotics, crisis management in military and humanitarian frameworks, among others. Practical routing applications are usually quite distinct from the academic cases, encompassing additional sets of specific constraints, objectives and decisions which breed further new problem variants. The resulting "Multi-Attribute" Vehicle Routing Problems (MAVRP) are the support of a vast literature which, however, lacks unified methods capable of addressing multiple MAVRP. In addition, some "rich" VRPs, i.e. those that involve several attributes, may be difficult to address because of the wide array of combined and possibly antagonistic decisions they require. This thesis contributes to address these challenges by means of problem structure analysis, new metaheuristics and unified method developments. The "winning strategies" of 64 state-of-the-art algorithms for 15 different MAVRP are scrutinized in a unifying review. Another analysis is targeted on "timing" problems and algorithms for adjusting the execution dates of a given sequence of tasks. Such methods, independently studied in different research domains related to routing, scheduling, resource allocation, and even isotonic regression are here surveyed in a multidisciplinary review. A Hybrid Genetic Search with Advanced Diversity Control (HGSADC) is then introduced, which combines the exploration breadth of population-based evolutionary search, the aggressive-improvement capabilities of neighborhood-based metaheuristics, and a bi-criteria evaluation of solutions based on cost and diversity measures. Results of remarkable quality are achieved on classic benchmark instances of the capacitated VRP, the multi-depot VRP, and the periodic VRP. Further extensions of the method to VRP variants with constraints on time windows, limited route duration, and truck drivers' statutory pauses are also proposed. New route and neighborhood evaluation procedures are introduced to manage penalized infeasible solutions w.r.t. to time-window and duration constraints. Tree-search procedures are used for drivers' rest scheduling, as well as advanced search limitation strategies, memories and decomposition phases. A dynamic programming-based neighborhood search is introduced to optimally select the depot, vehicle type, and first customer visited in the route during local searches. The notable contribution of these new methodological elements is assessed within two different metaheuristic frameworks. To further advance general-purpose MAVRP methods, we introduce a new component-based heuristic resolution framework and a Unified Hybrid Genetic Search (UHGS), which relies on modular self-adaptive components for addressing problem specifics. Computational experiments demonstrate the groundbreaking performance of UHGS. With a single implementation, unique parameter setting and termination criterion, this algorithm matches or outperforms all current problem-tailored methods from more than 180 articles, on 26 vehicle routing variants and 39 benchmark sets. To address rich problems, UHGS was included in a new parallel cooperative solution framework called "Integrative Cooperative Search (ICS)", based on problem decompositions, partial solutions integration, and global search guidance. This compendium of results provides a novel view on a wide range of MAVRP and timing problems, on efficient heuristic searches, and on general-purpose solution methods for combinatorial optimization problems