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
... improvement, ignoring the exploration of the search space. Random search is an example of a strategy which explores ... improved offspring. 1.1.3 Population-based Search Generally, an algorithm for solving optimization problems 1.1 ...
... improved along the deepest descending direction gradually through iterations as shown in Fig. 1.2. This point-to-point approach embraces the danger of failing in local optima. GA performs a multi-directional search by maintaining a ...
... improved offspring. There are three common genetic operators: crossover, mutation and selection. 1.2.4.1 Crossover Crossover is the main genetic operator. It operates 10 1 Multiobjective Genetic Algorithms FitnessEvaluation ...
... improve them. Moreover, quite often the system can reach the optimum easier if it is possible to “cross” an infeasible region (especially in non-convex feasible search spaces). 1.2.5.2 Repairing Strategy Repairing a chromosome means to ...
... improve the GA's ability to focus on the most promising areas. Following a more Lamarckian approach, firsta traditional hill-climbing routine could use the offspring as a starting point and perform quick and localized optimization ...
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 |