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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... created throughout an evolutionary process. This is a popular option in many GA. The method may work reasonably well ... create a repairing procedure. Liepins and his collaborators have shown, through empirical test of GA performance on ...
... create C(t) fromP(t) by crossover routine; create C(t) from P(t) by mutation routine; climb C(t) by local search routine; evaluate C(t) by decoding routine; select P(t + 1) fromP(t) and C(t) by selection routine; t ← t +1; end output ...
... create C(t) fromP(f) by crossover routine; create C(t ) from P(t ) by mutation routine; create C(t ) from P(f ) by immigration routine; evaluate C{t) by decoding routine; ift > u then auto-tuning pjy[ , pq and p j by PLC; select P(t + l) ...
... create Pareto E(P); evaluate eval(P) by fitness assignment routine; while (not terminating condition) do create C(t) fromP(t) by crossover routine; create C(t) from P(t) by mutation routine; calculate objectives fi (C),i = 1,···,q by ...
... create the next generation. In the veGA approach, the selection step in each generation becomes a loop; each time through the loop the appropriate fraction of the next generation, or subpopulations, is selected on the basis of each of ...
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 |