Network Models and Optimization: Multiobjective Genetic Algorithm ApproachNetwork models are critical tools in business, management, science and industry. Network Models and Optimization: Multiobjective Genetic Algorithm Approach presents an insightful, comprehensive, and up-to-date treatment of multiple objective genetic algorithms to network optimization problems in many disciplines, such as engineering, computer science, operations research, transportation, telecommunication, and manufacturing. Network Models and Optimization: Multiobjective Genetic Algorithm Approach extensively covers algorithms and applications, including shortest path problems, minimum cost flow problems, maximum flow problems, minimum spanning tree problems, travelling salesman and postman problems, location-allocation problems, project scheduling problems, multistage-based scheduling problems, logistics network problems, communication network problem, and network models in assembly line balancing problems, and airline fleet assignment problems. Network Models and Optimization: Multiobjective Genetic Algorithm Approach can be used both as a student textbook and as a professional reference for practitioners in many disciplines who use network optimization methods to model and solve problems. |
From inside the book
Results 1-5 of 90
... efficient algorithms suitable for complex nature of network optimization problems is the major focus of this research work. Generally, EAs involve following metaheuristic optimization algorithms, such as genetic algorithm (GA) ...
... efficient and more robust in locating optimal solution and reducing computational effort than other conventional heuristics. 3. Flexibility: GA provides us great flexibility to hybridize with domain-dependent heuristics to make an efficient ...
... each of the parameters and how to find the values efficiently are very important and promising areas of research on the GA. 1.3.1 Genetic Local Search The idea of combining GA and 1.3 Hybrid Genetic Algorithms 15 Hybrid Genetic Algorithms.
... efficiently are very important and promising areas of research of the GA. Usually, fixed parameters are used in most applications of the GA. The values for the parameters are determined with a set-and-test approach. Since GA is an ...
... efficient in terms of search speed and search quality of a GA than the GA without them. Yun and Gen [44] proposed adaptive genetic algorithm (aGA) using FLC. To adaptively regulate GA operators using FLC, they use the scheme of Song et ...
Contents
1 | |
49 | |
Logistics Network Models | 135 |
Communication Network Models | 229 |
Advanced Planning and Scheduling Models | 297 |
Project Scheduling Models | 419 |
Assembly Line Balancing Models | 477 |
Tasks Scheduling Models | 551 |
References | 604 |
Index | 687 |
Other editions - View all
Network Models and Optimization: Multiobjective Genetic Algorithm Approach Mitsuo Gen,Runwei Cheng,Lin Lin No preview available - 2008 |
Network Models and Optimization: Multiobjective Genetic Algorithm Approach Mitsuo Gen,Runwei Cheng,Lin Lin No preview available - 2010 |