05507nam 2200721Ia 450 991078211910332120230617040935.01-281-93474-79786611934743981-279-485-9(CKB)1000000000537798(EBL)1679472(OCoLC)879023572(SSID)ssj0000209451(PQKBManifestationID)11196632(PQKBTitleCode)TC0000209451(PQKBWorkID)10265627(PQKB)11772155(MiAaPQ)EBC1679472(WSP)00005470(Au-PeEL)EBL1679472(CaPaEBR)ebr10255771(CaONFJC)MIL193474(EXLCZ)99100000000053779820040611d2004 uy 0engur|n|---|||||txtccrNetwork-based distributed planning using coevolutionary algorithms[electronic resource] /Raj Subbu, Arthur C SandersonRiver Edge, NJ World Scientific20041 online resource (193 p.)Series in intelligent control and intelligent automation ;v. 13Description based upon print version of record.981-238-754-4 Includes bibliographical references (p. 159-168) and index.Contents ; Foreword ; Preface ; 1. Introduction ; 1.1 Motivation ; 1.2 Approach ; 1.3 Principal Contributions ; 1.4 Book Outline ; 2. Background and Related Work ; 2.1 Collaborative Manufacturing ; 2.1.1 Concurrent Engineering ; 2.1.2 Agile Manufacturing2.2 Combinatorial Optimization 2.2.1 Deterministic Algorithms ; 2.2.2 Stochastic Algorithms ; 2.3 Evolutionary Algorithms ; 2.3.1 Principal Techniques ; 2.3.2 Theory and Applications ; 2.3.3 Techniques for Constrained Optimization ; 2.3.4 Multi-Node Algorithms2.3.5 Techniques for Dynamic Environments 2.4 Agents ; 2.5 Distributed Problem Solving ; 3. Problem Formulation and Analysis ; 3.1 Introduction ; 3.2 General Problem Formulation ; 3.2.1 Constraints ; 3.2.2 Objectives ; 3.2.3 Optimization Problem ; 3.2.4 Complexity Analysis3.3 Printed Circuit Assembly Problem 3.3.1 Complexity Analysis ; 3.4 Algorithm Applicability Analysis ; 3.4.1 Rationale ; 3.4.2 Problem Structure ; 3.4.3 Evaluation of Alternative Algorithms ; 3.4.4 Discussion ; 4. Theory and Analysis of Evolutionary Optimization ; 4.1 Introduction4.2 Theoretical Foundation 4.2.1 Notation ; 4.2.2 General Algorithm ; 4.2.3 Basic Results ; 4.3 Convergence Analysis ; 4.3.1 Convergence for a Unimodal Objective ; 4.3.2 Convergence for a Bimodal Objective ; 5. Theory and Analysis of Distributed Coevolutionary Optimization5.1 Introduction In this book, efficient and scalable coevolutionary algorithms for distributed, network-based decision-making, which utilize objective functions are developed in a networked environment where internode communications are a primary factor in system performance. A theoretical foundation for this class of coevolutionary algorithms is introduced using techniques from stochastic process theory and mathematical analysis. A case study in distributed, network-based decision-making presents an implementation and detailed evaluation of the coevolutionary decision-making framework that incorporates diSeries in intelligent control and intelligent automation ;v. 13.Electronic data processingDistributed processingAlgorithmsIntelligent agents (Computer software)Electronic data processingDistributed processing.Algorithms.Intelligent agents (Computer software)004005.2/76005.276Subbu Raj1531936Sanderson A. C(Arthur C.)28247MiAaPQMiAaPQMiAaPQBOOK9910782119103321Network-based distributed planning using coevolutionary algorithms3777912UNINA