الصفحة 1
الصفحة 1
Multiobjective Problem Solving from Nature : From Concepts to Applications
he book focuses on how MOEAs and related techniques can be used to solve problems, particularly in the disciplines of science and engineering. Contributions by leading researchers show how the concepts of multiobjective optimization can be used to reformulate and resolve problems in broad areas such as constrained optimization, coevolution, classification, inverse modelling and design. The book is distinguished from other texts on MOEAs in that it is not primarily about the algorithms, nor specific applications, but about the concepts and processes involved in solving problems using a multiobjective approach. Each chapter contributes to the central, deep concepts and themes of the book: evaluating the utility of the multiobjective approach; discussing alternative problem formulations; showing how problem formulation affects the search process; and examining solution selection and decision making.
Multi-objective Management in Freight Logistics : Increasing Capacity, Service Level and Safety with Optimization Algorithms
Multi-objective Management in Freight Logistics provides decision makers with new methods and tools to implement multi-objective optimization models in logistics. The book combines theoretical aspects with applications, showing the advantages and the drawbacks of adopting scalarization techniques, and when it is worthwhile to reduce the problem to a goal programming one. The book also shows applications where more than one decision maker evaluates the effectiveness of the logistic system and thus a multilevel programming approach is sought to attain meaningful solutions.
Multi-Objective Machine Learning
This monograph presents a selected collection of research work on multi-objective approach to machine learning, including multi-objective feature selection, multi-objective model selection in training multi-layer perceptrons, radial-basis-function networks, support vector machines, decision trees, and intelligent systems.
Multi-Objective Evolutionary Algorithms for Knowledge Discovery from Databases
The present volume provides a collection of seven articles containing new and high quality research results demonstrating the significance of Multi-objective Evolutionary Algorithms (MOEA) for data mining tasks in Knowledge Discovery from Databases (KDD). These articles are written by leading experts around the world. It is shown how the different MOEAs can be utilized, both in individual and integrated manner, in various ways to efficiently mine data from large databases.
Metaheuristics for Scheduling in Distributed Computing Environments
This volume presents meta-heuristics approaches for Grid scheduling problems. Due to the complex nature of the problem, meta-heuristics are primary techniques for the design and implementation of efficient Grid schedulers. The volume brings new ideas, analysis, implementations and evaluation of meta-heuristic techniques for Grid scheduling, which make this volume novel in several aspects. The 13 chapters of this volume have identified several important formulations of the problem, which we believe will serve as a reference for the researchers in the Grid computing community.
Introduction to Applied Optimization
This text presents a multi-disciplined view of optimization, providing students and researchers with a thorough examination of algorithms, methods, and tools from diverse areas of optimization without introducing excessive theoretical detail. This second edition includes additional topics, including global optimization and a real-world case study using important concepts from each chapter.
Internet and network economics ; 4th International Workshop, WINE 2008, Shanghai, China, December 17-20, 2008. Proceedings
This book constitutes the refereed proceedings of the 4th International Workshop on Internet and Network Economics, WINE 2008, held in Shanghai, China, in December 2008.The 68 revised full papers presented together with 10 invited talks were carefully reviewed and selected from 126 submissions. The papers are organized in topical sections on market equilibrium, congestion games, information markets, nash equilibrium, network games, solution concepts, algorithms and optimization, mechanism design, equilibrium, online advertisement, sponsored search auctions, and voting problems.
Internet and Network Economics ; 3rd International Workshop,WINE 2007, San Diego, CA, USA, December 12-14, 2007, Proceedings
This book constitutes the refereed proceedings of the Third International Workshop on Internet and Network Economics, WINE 2007, held in San Diego, CA, USA, in December 2007.
Hybrid metaheuristics ; Vol. 4030 ; 3rd International Workshop, HM 2006, Gran Canaria, Spain, October 13-14, 2006, Proceedings
The selection of papers for HM 2006 consolidated some of the mainstream issues that have emerged from the past editions. Firstly, there are prominent examples of e?ective hybrid techniques whose design and implementation were motivated by challenging real-world applications. We believe this is particularly important for two reasons: on the one hand, researchers are conscious that the primary goal of developing algorithms is to solve relevant real-life problems; on the other hand, the path towarde?cient solving methods for practical problems is a source of new outstanding ideas and theories. A second important issue is that the research community on metaheur- tics has become increasingly interested in and open to techniques and methods known from arti?cial intelligence (AI) and operations research (OR). So far, the most representative examples of such integration have been the use of AI/OR techniques as subordinates of metaheuristic methods. As a historical and - ymological note, this is in perfect accordance with the original meaning of a metaheuristic as a “general strategy controlling a subordinate heuristic. ” The awareness of the need for a sound experimental methodology is a third keypoint.
Graph theory, combinatorics and algorithms: interdisciplinary applications
focuses on discrete mathematics and combinatorial algorithms interacting with real world problems in computer science, operations research, applied mathematics and engineering. The book contains eleven chapters written by experts in their respective fields, and covers a wide spectrum of high-interest problems across these discipline domains.The chapters focus on "real world" applications, all of which will be of considerable interest across the areas of Operations Research, Computer Science, Applied Mathematics, and Engineering. These problems include Internet congestion control, high-speed communication networks, multi-object auctions, resource allocation, software testing, data structures, etc. In sum, this is a book focused on major, contemporary problems, written by the top research scholars in the field, using cutting-edge mathematical and computational techniques.
Generalized Convexity, Generalized Monotonicity and Applications ; Proceedings of the 7th International Symposium on Generalized Convexity and Generalized Monotonicity
This volume contains a collection of refereed articles on generalized convexity and generalized monotonicity. The first part of the book contains invited papers with applications of (generalized) convexity to such diverse fields as algebraic dynamics of the Gamma function values, discrete optimization, Lipschitzian stability of parametric constraint systems, and monotonicity of functions. The second part contains contributions presenting the latest developments in generalized convexity and generalized monotonicity: its connections with discrete and with continuous optimization, multiobjective optimization, fractional programming, nonsmooth Aanalysis, variational inequalities, and its applications to concrete problems such as finding equilibrium prices in mathematical economics, or hydrothermal scheduling.
Generalized Convexity and Related Topics
The book contains invited papers by well-known experts on a wide range of topics (economics, variational analysis, probability etc.) closely related to convexity and generalized convexity, and refereed contributions of specialists from the world on current research on generalized convexity and applications, in particular, to optimization, economics and operations research.
Fuzzy multi-criteria decision making : Theory and applications with recent developments
In trying to make a satisfactory decision when imprecise and multicriteria situations are involved, a decision maker has to use a fuzzy multicriteria decision making method. Fuzzy Multi-Criteria Decision Making (MCDM) presents fuzzy multiattribute and multiobjective decision-making methodologies by distinguished MCDM researchers. In summarizing the concepts and results of the most popular fuzzy multicriteria methods, using numerical examples, this work examines all the fuzzy multicriteria methods recently developed, such as fuzzy AHP, fuzzy TOPSIS, interactive fuzzy multiobjective stochastic linear programming, fuzzy multiobjective dynamic programming, grey fuzzy multiobjective optimization, fuzzy multiobjective geometric programming, and more.
Frontiers in Computing Technologies for Manufacturing Applications
Frontiers in Computing Technologies for Manufacturing Applications presents an overview of the state-of-the-art intelligent computing in manufacturing. Modeling, data processing, algorithms and computational analysis of difficult problems found in advanced manufacturing are discussed. It is the first book to bring together combinatorial optimization, information systems and fault diagnosis and monitoring in a consistent manner. Techniques are presented in order to aid decision makers needing to consider multiple, conflicting objectives in their decision processes. In particular, the use of metaheuristic optimization techniques for multi-objective problems is discussed. Readers will learn about computational technologies that can improve the performance of manufacturing systems ranging from manufacturing equipment to supply chains.
Foundations of genetic algorithms ; 8th International Workshop, FOGA 2005, Aizu-Wakamatsu City, Japan, January 5-9, 2005, Revised Selected Papers
The8thWorkshopontheFoundationsofGeneticAlgorithms,FOGA-8,washeld at the University of Aizu in Aizu-Wakamatsu City, Japan, January 5–9, 2005. This series of workshops was initiated in 1990 to encourage further research on the theoretical aspects of genetic algorithms, and the workshops have been held biennially ever since. The papers presented at these workshops are revised, edited and published as volumes during the year following each workshop. This series of (now eight) volumes provides an outstanding source of reference for the theoretical work in this ?eld. At the same time this series of volumes provides a clear picture of how the theoretical research has grown and matured along with the ?eld to encompass many evolutionary computation paradigms including evolution strategies (ES), evolutionary programming (EP), and genetic programming (GP), as well as the continuing growthininteractionswith other ?elds suchas mathematics,physics, and biology.
Evolutionary Scheduling
Evolutionary scheduling is a vital research domain at the interface of two important sciences - artificial intelligence and operational research. Scheduling problems are generally complex, large scale, constrained, and multi-objective in nature, and classical operational research techniques are often inadequate at solving them effectively. With the advent of computation intelligence, there is renewed interest in solving scheduling problems using evolutionary computational techniques. These techniques, which include genetic algorithms, genetic programming, evolutionary strategies, memetic algorithms, particle swarm optimization, ant colony systems, etc, are derived from biologically inspired concepts and are well-suited to solve scheduling problems since they are highly scalable and flexible in terms of handling constraints and multiple objectives. This edited book gives an overview of many of the current developments in the large and growing field of evolutionary scheduling, and demonstrates the applicability of evolutionary computational techniques to solve scheduling problems, not only to small-scale test problems, but also fully-fledged real-world problems.
Evolutionary Multi-Criterion Optimization ; 3rd International Conference, EMO 2005, Guanajuato, Mexico, March 9-11, 2005, Proceedings
Constitutes the refereed proceedings of the Third International Conference on Evolutionary Multi-Criterion Optimization, EMO 2005, held in Guanajuato, Mexico, in March 2005.
Evolutionary Algorithms for Solving Multi-Objective Problems
This book for Solving Multi-Objective Problems, significantly augmented with contemporary knowledge and adapted for the classroom. All the various features of multi-objective evolutionary algorithms (MOEAs) are presented in an innovative and student-friendly fashion, incorporating state-of-the-art research results. The diversity of serial and parallel MOEA structures are given, evaluated and compared. The book provides detailed insight into the application of MOEA techniques to an array of practical problems. The assortment of test suites are discussed along with the variety of appropriate metrics and relevant statistical performance techniques.
Deep Statistical Comparison for Meta-heuristic Stochastic Optimization Algorithms
Presents a comprehensive comparison of the performance of stochastic optimization algorithms / Includes an introduction to benchmarking and statistical analysis / Provides a web-based tool for making statistical comparisons of optimization algorithms / Overviews of the current approaches used to analyze algorithm performance in a range of common scenarios, while also addressing issues that are often overlooked. In turn, it shows how these issues can be easily avoided by applying the principles that have produced Deep Statistical Comparison and its variants. The focus is on statistical analyses performed using single-objective and multi-objective optimization data. At the end of the book, examples from a recently developed web-service-based e-learning tool (DSCTool) are presented. The tool provides users with all the functionalities needed to make robust statistical comparison analyses in various statistical scenarios. The book is intended for newcomers to the field and experienced researchers alike. For newcomers, it covers the basics of optimization and statistical analysis, familiarizing them with the subject matter before introducing the Deep Statistical Comparison approach. Experienced researchers can quickly move on to the content on new statistical approaches.
Computational Intelligence in Reliability Engineering : Evolutionary Techniques in Reliability Analysis and Optimization
This book covers the recent applications of computational intelligence techniques in reliability engineering. This volume contains a survey of the contributions made to the optimal reliability design literature in the resent years and chapters devoted to different applications of a genetic algorithm in reliability engineering and to combinations of this algorithm with other computational intelligence techniques. Genetic algorithms are one of the most widely used metaheuristics, inspired by the optimization procedure that exists in nature, the biological phenomenon of evolution.
