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Multi-Objective Optimization in Theory and Practice II : metaheuristic algorithms.

Author: André A Keller
Publisher: Sharjah : Bentham Science Publishers, 2019.
Edition/Format:   eBook : Document : EnglishView all editions and formats
Summary:
Multi-Objective Optimization in Theory and Practice is a simplified two-part approach to multi-objective optimization (MOO) problems. This second part focuses on the use of metaheuristic algorithms in more challenging practical cases. The book includes ten chapters that cover several advanced MOO techniques. These include the determination of Pareto-optimal sets of solutions, metaheuristic algorithms, genetic search  Read more...
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Genre/Form: Electronic books
Additional Physical Format: Print version:
Keller, André A.
Multi-Objective Optimization in Theory and Practice II: Metaheuristic Algorithms.
Sharjah : Bentham Science Publishers, ©2019
Material Type: Document, Internet resource
Document Type: Internet Resource, Computer File
All Authors / Contributors: André A Keller
ISBN: 1681087057 9781681087054
OCLC Number: 1097974294
Notes: 7.2.2. Decomposition-Based MOEA Algorithm
Description: 1 online resource (310 pages)
Contents: Cover; Title; Biblography; End User License Agreement; Contents; Preface; Acknowledgements; Pareto-Optimal Front Determination; Pareto-Optimal Front Determination; 1.1. INTRODUCTION; 1.1.1. Heuristic and Metaheuristic Algorithms; 1.1.2. History of Metaheuristics; 1.1.3. Probabilistic Metaheuristics and Applications; 1.1.4. Optimum Design of Framed Structures: A Review of Literature; 1.2. Elements of Static Multi-Objective Programming; 1.2.1. Problem Formulation; 1.2.2. Concept of Dominance; 1.2.3. Pareto-Optimality; 1.3. Pareto-Optimal Front; 1.3.1. Non-Dominated Solutions 1.3.2. Analytical Pareto-Optimal Front1.3.3. Near Pareto-Optimal Front; 1.3.4. Shapes of a Pareto-Optimal Front; 1.4. Selection Procedures of Algorithms; 1.4.1. Elitist Pareto Criteria; 1.4.2. Non-Pareto Criteria; 1.4.3. Bi-criterion Evolution; 1.4.4. Other Concepts of Dominance; NOTES; REFERENCES; Untitled; Metaheuristic Optimization Algorithms; Metaheuristic Optimization Algorithms; 2.1. INTRODUCTION; 2.2. Simulated Annealing Algorithm; 2.2.1. Annealing Principle and Description; 2.2.2. Problem Formulation; 2.2.3. Algorithm Description; 2.3. Multi-Objective Simulated Annealing 2.3.1. MOSA Algorithms2.3.2. Test Problems; NOTES; REFERENCES√; Evolutionary Strategy Algorithms; Evolutionary Strategy Algorithms; 3.1. INTRODUCTION1; 3.2. Principles and Operators; 3.2.1. Algorithm for Solving Optimization Problems; 3.2.2. Binary and Real-Number Encoding; 3.2.3. Genetic Operators; 3.3. GA-Based Mathematica® Notebook; 3.4. Single-Objective Optimization; 3.4.1. SciLab Package for Genetic Algorithm; 3.4.2. GA-Based Software Package: GENOCOP III; NOTES; REFERENCES√; Genetic Search Algorithms; Genetic Search Algorithms; 4.1. INTRODUCTION 4.2. Niched Pareto Genetic Algorithms (NPGA)4.3. Non-Dominated Sorting Genetic Algorithm; 4.4. Multi-Objective Optimization Test Problems; 4.4.1. Unconstrained Optimization Problems; 4.4.2. Constrained Optimization Problem; NOTES; REFERENCES√; Evolution Strategy Algorithms; Evolution Strategy Algorithms; 5.1. INTRODUCTION; 5.2. Differential Evolution Strategy; 5.2.1. Principles and Algorithm2; 5.2.2. DE Operators; 5.3. DE Algorithm for Single-Objective Optimization Problems; 5.4. Multi-Objective DE Algorithm; 5.4.1. Diversity-Promoting; 5.4.2. Performing Elitism; NOTES; REFERENCES√ Swarm Intelligence and Co-Evolutionary AlgorithmsSwarm Intelligence and Co-Evolutionary Algorithms; 6.1. INTRODUCTION; 6.2. Particle Swarm Optimization; 6.3. Cooperative Co-Evolutionary Genetic Algorithms; 6.4. Competitive Predator-Prey Optimization Model; 6.4.1. Principle of PP Algorithm; 6.4.2. PP Algorithm; 6.4.3. Illustrative Problems; NOTES; REFERENCES√; Decomposition-Based and Hybrid Evolutionary Algorithms; Decomposition-Based and Hybrid Evolutionary Algorithms; 7.1. INTRODUCTION; 7.2. Decomposition-Based Algorithm; 7.2.1. Scalar Decomposition Principle

Abstract:

Multi-Objective Optimization in Theory and Practice is a simplified two-part approach to multi-objective optimization (MOO) problems. This second part focuses on the use of metaheuristic algorithms in more challenging practical cases. The book includes ten chapters that cover several advanced MOO techniques. These include the determination of Pareto-optimal sets of solutions, metaheuristic algorithms, genetic search algorithms and evolution strategies, decomposition algorithms, hybridization of different metaheuristics, and many-objective (more than three objectives) optimization and parallel computation. The final section of the book presents information about the design and types of fifty test problems for which the Pareto-optimal front is approximated. For each of them, the package NSGA-II is used to approximate the Pareto-optimal front. It is an essential handbook for students and teachers involved in advanced optimization courses in engineering, information science and mathematics degree programs.

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