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Multi-objective Optimization.

Author: Gade Pandu Rangaiah
Publisher: Singapore : World Scientific Publishing Company, 2016.
Series: Advances in process systems engineering.
Edition/Format:   eBook : Document : EnglishView all editions and formats
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Genre/Form: Electronic books
Additional Physical Format: Print version:
Rangaiah, Gade Pandu.
Multi-objective Optimization: Techniques And Applications In Chemical Engineering (Second Edition).
Singapore : World Scientific Publishing Company, ©2016
Material Type: Document, Internet resource
Document Type: Internet Resource, Computer File
All Authors / Contributors: Gade Pandu Rangaiah
ISBN: 9789813148239 9813148233 9789813148246 9813148241
OCLC Number: 972289794
Notes: 4.5 Results and Discussion (Metrics for the Comparison of Results).
Description: 1 online resource (588 pages).
Contents: Preface to the Second Edition; Preface to the First Edition; Chapter 1 Introduction; 1.1 Process Optimization; 1.2 Multi-Objective Optimization: Basics; 1.3 Multi-Objective Optimization: Methods; 1.4 Alkylation Process Optimization for Two Objectives; 1.4.1 Alkylation Process and its Model; 1.4.2 Multi-Objective Optimization Results and Discussion; 1.5 Scope and Organization of the Book; References; Exercises; Chapter 2 Multi-Objective Optimization Applications in Chemical Engineering; 2.1 Introduction; 2.2 Process Design and Operation; 2.3 Biotechnology and Food Industry. 2.4 Petroleum Refining and Petrochemicals2.5 Pharmaceuticals and Other Products/Processes; 2.6 Polymerization; 2.7 Conclusions; References; Chapter 3 Multi-Objective Evolutionary Algorithms: A Review of the State-of-the-Art and some of their Applications in Chemical Engineering; 3.1 Introduction; 3.2 Basic Concepts; 3.2.1 Pareto Optimality; 3.3 The Early Days; 3.4 Modern MOEAs; 3.5 MOEAs in Chemical Engineering; 3.6 MOEAs Originated in Chemical Engineering; 3.6.1 Neighborhood and Archived Genetic Algorithm; 3.6.2 Criterion Selection MOEAs; 3.6.3 The Jumping Gene Operator. 3.6.4 Multi-Objective Differential Evolution3.7 Some Applications Using Well-Known MOEAs; 3.7.1 TYPE I: Optimization of an Industrial Nylon 6 Semibatch Reactor; 3.7.2 TYPE I: Optimization of an Industrial Ethylene Reactor; 3.7.3 TYPE II: Optimization of an Industrial Styrene Reactor; 3.7.4 TYPE II: Optimization of an Industrial Hydrocracking Unit; 3.7.5 TYPE III: Optimization of Semi-Batch Reactive Crystallization Process; 3.7.6 TYPE III: Optimization of Simulated Moving Bed Process; 3.7.7 TYPE IV: Biological and Bioinformatics Problems. 3.7.8 TYPE V: Optimization of a Waste Incineration Plant3.7.9 TYPE V: Chemical Process Systems Modelling; 3.8 Critical Remarks; 3.9 Additional Resources; 3.10 Future Research; 3.11 Conclusions; Acknowledgements; References; Chapter 4 Multi-Objective Genetic Algorithm and Simulated Annealing with the Jumping Gene Adaptations; 4.1 Introduction; 4.2 Genetic Algorithm; 4.2.1 Simple GA (SGA) for Single-Objective Problems; 4.2.2 Multi-Objective Elitist Non-Dominated Sorting GA (NSGA-II) and its JG Adaptations; 4.2.2.1 Jumping Genes/Transposons (Stryer, 2000). 4.2.2.2 (Variable-Length) Binary-Coded NSGA-II-JG (Kasat and Gupta, 2003)4.2.2.3 (Fixed-Length) NSGA-II-aJG; 4.2.2.4 NSGA-II-mJG ('modified' JG); 4.2.2.5 NSGA-II-saJG ('specific adapted' JG); 4.2.2.6 NSGA-II-sJG ('specific' JG); 4.2.3 Jumping gene operators for real-coded GA; 4.3 Simulated Annealing (SA); 4.3.1 Simple Simulated Annealing (SSA) for Single-Objective Problems; 4.3.2 Multi-Objective Simulated Annealing (MOSA); 4.3.2.1 MOSA-JG; 4.3.2.2 MOSA-aJG; 4.4 Application of the Jumping Gene Adaptations of NSGA-II and MOSA to Three Benchmark Problems.
Series Title: Advances in process systems engineering.

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