| Description |
1 online resource. |
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text file |
| Series |
Engineering tools, techniques and tables
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Engineering tools, techniques and tables.
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| Bibliography |
Includes bibliographical references and index. |
| Contents |
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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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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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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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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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. |
| Local Note |
eBooks on EBSCOhost EBSCO eBook Subscription Academic Collection - North America |
| Language |
English. |
| Subject |
Swarm intelligence.
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Swarm intelligence. |
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Mathematical optimization.
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Mathematical optimization. |
| Genre/Form |
Electronic books.
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| Added Author |
Olsson, Andrea E.
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| Other Form: |
Print version: Particle swarm optimization Hauppauge, N.Y. : Nova Science Publishers, c2011. 9781616685270 (hardcover) (DLC) 2010012247 |
| ISBN |
9781613247006 ebook |
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1613247001 |
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9781616685270 hardcover |
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1616685271 (hardcover) |
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