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 6-10 of 89
... objective optimization problems. The basic feature of the GA is the multiple directional and global search by maintaining a population of potential solutions from generation to generation. The population-to-population approach is useful ...
... objective optimization problems presents a formidable theoretical and practical challenge to the mathematical ... objectives fi (P),i = 1,···,q by decoding routine; create Pareto E(P); evaluate eval(P) by fitness assignment routine ...
... objectives and, simultaneously, provides the appropriate probabilities for multiple selection of individuals which are better than average on more than one objective. Fig. 1.14 Illustration of veGA selection A simple two objective ...
... objective problem with one variable is used to test the properties of veGA by Schaffer: min f1 (x)=x 2 min f2 (x)=(x ... objective optimizations to the GA. Even though veGA cannot give a satisfactory solution to the multiple objective ...
... objective function and combines the weighted objectives into a single objective function. Typically, there are two kinds of search behaviors in the objective space: fixed direction search and multiple direction search as demonstrated in ...
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 |