Intelligent Evolutionary Optimization introduces biologically-inspired intelligent optimization algorithms to address complex optimization problems and provide practical solutions for tackling combinatorial optimization problems. The book explores efficient search and optimization methods in high-dimensional spaces, particularly for high-dimensional multi-objective optimization problems, offering practical guidance and effective solutions across various domains. Providing practical solutions, methods, and tools to tackle complex optimization problems and enhance modern optimization techniques, this book will be a valuable resource for professionals seeking to enhance their understanding and proficiency in intelligent evolutionary optimization. . Introduces biologically-inspired intelligent optimization algorithms capable of effectively solving complex optimization problems, teaching readers how to apply these algorithms and improve existing optimization techniques. Explores multi-objective optimization problems in high-dimensional spaces for readers to understand how to perform efficient search and optimization, acquiring strategies and tools adapted to high-dimensional environments. Presents the practical applications of intelligent evolutionary optimization in various fields to help readers gain insights into the latest trends and application scenarios in the field and receive practical guidance and solutions INDICE: Part I: Evolutionary Algorithm for Many-Objective Optimization1. Preliminary2. A New Dominance Relation Based Evolutionary Algorithm for Many-Objective Optimization3. Balancing Convergence and Diversity in Decomposition-Based Many-Objective Optimizers4. Objective Reduction in Many-Objective Optimization: Evolutionary Multi-objective Approach and Critical5. Expensive Multi-objective Evolutionary Optimization Assisted by Dominance PredictionPart II: Heuristic Algorithm for Flexible Job Shop Scheduling Problem6. Preliminary7. A Hybrid Harmony Search Algorithm for the Flexible Job Shop Scheduling Problem8. Flexible Job Shop Scheduling Using Hybrid Differential Evolution Algorithms9. An Integrated Search Heuristic for Large-scale Flexible Job Shop Scheduling Problems10. Multi-objective Flexible Job Shop Scheduling Using Memetic Algorithms
- ISBN: 978-0-443-27400-8
- Editorial: Elsevier
- Encuadernacion: Rústica
- Páginas: 386
- Fecha Publicación: 22/04/2024
- Nº Volúmenes: 1
- Idioma: Inglés