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
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... improve improve Global optimum Global optimum ove ove fitness fitness impr Local optimum impr Local optimum Search range Search range for local search for local search Solution by GA Solution by GA Mp 8.0 5.0 2.0 22u u2 0 fΔ α α−u2 ...
... improve it. Its main scheme is to use two FLCs: the crossover FLC and mutation FLC are implemented independently to regulate the rates of crossover and mutation operators adaptively during the genetic search process. The heuristic ...
... improve its performance further. The mean population size ̄n of the periodic scheme corresponds to the constant population size GA having the same computing cost. Moreover, the scheme is characterized by the amplitude D and the period ...
... improvement in any objective function is possible without sacrificing at least one of the other objective functions. For a given nondominated point in the criterion space Z, its image point in the decision space S is called efficient or ...
... improved adaptive-weight fitness as- signment approach with the consideration of the disadvantages of weighted-sum approach and Pareto ranking-based approach. First, there are two extreme points defined as the maximum extreme point z+ ...
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 |