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Metaheuristics for Machine Learning
Using metaheuristics to enhance machine learning techniques has become trendy and has achieved major successes in both supervised (classification and regression) and unsupervised (clustering and rule mining) problems. Furthermore, automatically generating programs via metaheuristics, as a form of evolutionary computation and swarm intelligence, has now gained widespread popularity. This book investigates different ways of integrating metaheuristics into machine learning techniques, from both theoretical and practical standpoints. It explores how metaheuristics can be adapted in order to enhance machine learning tools and presents an overview of the main metaheuristic programming methods. Moreover, real-world applications are provided for illustration, e.g., in clustering, big data, machine health monitoring, underwater sonar targets, and banking.
Natural Intelligence for Scheduling, Planning and Packing Problems
Scheduling, planning and packing are ubiquitous problems that can be found in a wide range of real-world settings. These problems transpire in a large variety of forms, and have enormous socio-economic impact. For many years, significant work has been devoted to automating the processes of scheduling, planning and packing using different kinds of methods. However, poor scaling and the lack of flexibility of many of the conventional methods coupled with the fact that most of the real-world problems across the application areas of scheduling, planning and packing nowadays tend to be of large scale, dynamic and full of complex dependencies have made it necessary to tackle them in unconventional...
Stochastic Global Optimization
Optimization has played a key role in the design, planning and operation of chemical and related processes, for several decades. Global optimization has been receiving considerable attention in the past two decades. Of the two types of techniques for global optimization, stochastic global optimization is applicable to any type of problems having non-differentiable functions, discrete variables and/or continuous variables. It, thus, shows significant promise and potential for process optimization. So far, there are no books focusing on stochastic global optimization and its applications in chemical engineering. Stochastic Global Optimization ? a monograph with contributions by leading researchers in the area ? bridges the gap in this subject, with the aim of highlighting and popularizing stochastic global optimization techniques for chemical engineering applications. The book, with 19 chapters in all, is broadly categorized into two sections that extensively cover the techniques and the chemical engineering applications.
Industrial Transformation
This book focuses on industrial development, design, implementation, and transformation using technologies such as Artificial Intelligence, Machine Learning, the Internet of Things (IoT), Big Data Analysis, and Blockchain. It incorporates complex processes, functions, and various other elements as one central component of digital systems. Industrial Transformation: Implementation and Essential Components and Processes of Digital Systems discusses the industry transformation aligned with the computerization of manufacturing and the required skills needed to build a new workforce. This book covers the role that AI plays in the management of resource flow and decision-making in the transformati...
Computational Intelligence in Flow Shop and Job Shop Scheduling
For over fifty years now, the famous problem of flow shop and job shop scheduling has been receiving the attention of researchers in operations research, engineering, and computer science. Over the past several years, there has been a spurt of interest in computational intelligence heuristics and metaheuristics for solving this problem. This book seeks to present a study of the state of the art in this field and also directions for future research.
Metaheuristics for Production Scheduling
This book describes the potentialities of metaheuristics for solving production scheduling problems and the relationship between these two fields. For the past several years, there has been an increasing interest in using metaheuristic methods to solve scheduling problems. The main reasons for this are that such problems are generally hard to solve to optimality, as well as the fact that metaheuristics provide very good solutions in a reasonable time. The first part of the book presents eight applications of metaheuristics for solving various mono-objective scheduling problems. The second part is itself split into two, the first section being devoted to five multi-objective problems to which...
Métaheuristiques pour l'ordonnancement multicritère et les problèmes de transport
Pour assurer une production de biens de qualité, de manière fiable et dans des délais maîtrisés, les organisations ont besoin d’outils d'exécution optimale de tâches tels que l’ordonnancement. Le succès des méthodologies de résolution des problèmes d’ordonnancement de production basées sur les métaheuristiques s’explique par leur capacité à fournir des solutions proches de l’optimum, dans des temps raisonnables. Cet ouvrage se consacre, dans un premier temps, aux métaheuristiques appliquées aux problèmes d’ordonnancement multicritère, qui sont des cas particuliers des problèmes d’optimisation combinatoire multicritère, généralement NP-difficiles. Puis, il s’intéresse aux préoccupations d’ordonnancement dans le secteur du transport qui suscitent également de multiples problèmes d’optimisation. Deux grands domaines d’application se distinguent, celui des systèmes de transport et celui des ressources de transport intervenant dans un atelier.
Métaheuristiques pour l'ordonnancement monocritère des ateliers de production
Pour assurer une production de biens de qualité, de manière fiable et dans des délais maîtrisés, les organisations ont besoin d’outils d'exécution optimale de tâches tels que l’ordonnancement. Le succès des méthodologies de résolution des problèmes d’ordonnancement de production basées sur les métaheuristiques s’explique par leur capacité à fournir des solutions proches de l’optimum, dans des temps raisonnables. Cet ouvrage présente des exemples concrets d’applications des métaheuristiques pour la résolution des problèmes d’ordonnancement monocritère. Il expose notamment différents cas de problèmes de type flowshop, les job-shops flexibles, la production sur machines parallèles ou le problème d’ordonnancement avec des pénalités d’avance et de retard.
Stochastic Global Optimization
Ch. 1. Introduction / Gade Pandu Rangaiah -- ch. 2. Formulation and illustration of Luus-Jaakola optimization procedure / Rein Luus -- ch. 3. Adaptive random search and simulated annealing optimizers : algorithms and application issues / Jacek M. Jezowski, Grzegorz Poplewski and Roman Bochenek -- ch. 4. Genetic algorithms in process engineering : developments and implementation issues / Abdunnaser Younes, Ali Elkamel and Shawki Areibi -- ch. 5. Tabu search for global optimization of problems having continuous variables / Sim Mong Kai, Gade Pandu Rangaiah and Mekapati Srinivas -- ch. 6. Differential evolution : method, developments and chemical engineering applications / Chen Shaoqiang, Gade ...
Metaheuristics for Production Scheduling
This book describes the potentialities of metaheuristics for solving production scheduling problems and the relationship between these two fields. For the past several years, there has been an increasing interest in using metaheuristic methods to solve scheduling problems. The main reasons for this are that such problems are generally hard to solve to optimality, as well as the fact that metaheuristics provide very good solutions in a reasonable time. The first part of the book presents eight applications of metaheuristics for solving various mono-objective scheduling problems. The second part is itself split into two, the first section being devoted to five multi-objective problems to which...
