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|a UAMI
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| 245 |
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|a Particle swarm optimization :
|b theory, techniques, and applications /
|c Andrea E. Olsson, editor.
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| 264 |
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|a Hauppauge, N.Y. :
|b Nova Science Publishers,
|c c2011.
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| 300 |
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|a 1 online resource.
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| 336 |
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|a text
|b txt
|2 rdacontent
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| 337 |
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|a computer
|b c
|2 rdamedia
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| 338 |
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|a online resource
|b cr
|2 rdacarrier
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| 490 |
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|a Engineering tools, techniques and tables
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|a Includes bibliographical references and index.
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| 588 |
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|a Description based on print version record and CIP data provided by publisher.
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| 546 |
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|a English.
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|a PARTICLE SWARM OPTIMIZATION: THEORY, TECHNIQUES AND APPLICATIONS; PARTICLE SWARM OPTIMIZATION: THEORY, TECHNIQUES AND APPLICATIONS ; CONTENTS ; PREFACE ; USING MONO-OBJECTIVE AND MULTI-OBJECTIVEPARTICLE SWARM OPTIMIZATION FOR THE TUNINGOF PROCESS CONTROL LAWS; Abstract; I. Introduction; II. Choice for the Use of Particle Swarm Optimization; III. Mono-objective PSO for the Design of Control Laws; III. 1. Definition of Optimization Problems; III. 2. Tuning of PID Controllers; III. 3. Reduced Order H Control; III. 3.1. H Control; III. 3.2. Full Order H Synthesis.
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|a III. 3.3. Reduced Order H SynthesisIII. 3.4. Case Study; III. 3.5. One Output H Synthesis; III. 3.6. Three Output H Synthesis; III. 4. Non Linear Predictive Control; IV. Multi-objective PSO for the Design of Controllers; V. Conclusions; References; STUDY ON VEHICLE ROUTING PROBLEM WITH TIMEWINDOWS BASED ON ENHANCED PARTICLESWARM OPTIMIZATION APPROACH; Abstract; 1. Introduction; 2. Problem Solving Methodology; 2.1. The Basic Particle Swarm Optimization; 2.2. The Vehicle Routing Problem with Time Windows Problem; 2.3. The Difficulty for Using Basic PSO to Solve the VRPTW.
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|a 2.4. The New Solution Strategies of Predicting Particle Swarm OptimizationStrategy I: Search Space Transformation; Strategy II: Boundary Constraint Handling; Strategy III: Forward Predicting Update on Velocity Update Equation; 3. The Algorithm of Predicting PSO; Phase I: Candidate Customer Search in Initial Stage; Phase II: Global Initial Solution Generated Through Feasibility Test; Phase III. Population Search for Optimization; 4. Computational Experiment; 4.1. Problem Data; 4.2. Computational Results; 5. Conclusion; Reference.
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|a RELIABILITY OPTIMIZATION PROBLEMS USINGADAPTIVE GENETIC ALGORITHM AND IMPROVEDPARTICLE SWARM OPTIMIZATIONAbstract; 1. Introduction; 2. Reliability Optimization Problems; 3. Hybrid Approach Using iGA and iPSO; 3.1. iGA Design; 3.2. iPSO Design; 3.3. Hybrid Approach; 3.4. Overall Procedure; 4. Numerical Examples; 4.1. Test Problems; 4.1.1. Test Problem 1 (T-1); 1) Case 1; 2) Case 2; 3) Case 3; 4.1.2. Test Problem 2 (T-2); 4.2. Test Results; 5. Conclusion; References; CONVERGENCE ISSUES IN PARTICLESWARM OPTIMIZATION; Abstract; Introduction; Managing Population and Exploration.
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|a Managing Velocity and Search RegionManaging Optima and Resuming Exploration; Convergence in Optimization Algorithms Otherthan PSO -- Overview; Convergence Analysis and Discussion; Terminology; Convergence as a Stopping Condtion; Conclusion; Convergence Methods; Guidelines for Convergence; MAV Convergence/Stopping Point Pseudo-Code; Unimodal Problems; Multimodal Problems; Epilogue; References; GLOBALLY CONVERGENT MODIFICATIONS OF PARTICLE SWARM OPTIMIZATION FOR UNCONSTRAINED OPTIMIZATION; Abstract; 1. Introduction; 2.A Generalized Scheme for PSO; 3. Issues on the Parameters Assessment in PSO.
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| 590 |
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|a eBooks on EBSCOhost
|b EBSCO eBook Subscription Academic Collection - Worldwide
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| 650 |
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|a Swarm intelligence.
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| 650 |
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|a Mathematical optimization.
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| 650 |
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|a Optimisation mathématique.
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| 650 |
|
7 |
|a COMPUTERS
|x Enterprise Applications
|x Business Intelligence Tools.
|2 bisacsh
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| 650 |
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7 |
|a COMPUTERS
|x Intelligence (AI) & Semantics.
|2 bisacsh
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| 650 |
|
7 |
|a Mathematical optimization
|2 fast
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| 650 |
|
7 |
|a Swarm intelligence
|2 fast
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| 700 |
1 |
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|a Olsson, Andrea E.
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| 776 |
0 |
8 |
|i Print version:
|t Particle swarm optimization
|d Hauppauge, N.Y. : Nova Science Publishers, c2011.
|z 9781616685270 (hardcover)
|w (DLC) 2010012247
|
| 830 |
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0 |
|a Engineering tools, techniques and tables.
|
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