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

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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 te...

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Detalles Bibliográficos
Clasificación:Libro Electrónico
Autor principal: Keller, André A.
Formato: Electrónico eBook
Idioma:Inglés
Publicado: Sharjah : Bentham Science Publishers, 2019.
Temas:
Algorithms.
Metaheuristics.
Computer algorithms.
Algorithmes.
Métaheuristiques.
algorithms.
Computer algorithms
Algorithms
Metaheuristics
Acceso en línea:Texto completo
Tabla de Contenidos:
  • 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

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