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. |
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... routine; evaluate P(t) by decoding routine; while (not terminating condition) do create C(t) fromP(t) by crossover routine; create C(t) from P(t) by mutation routine; evaluate C(t) by decoding routine; select P(t + 1) fromP(t) and C(t) ...
... routine could use the offspring as a starting point and perform quick and localized optimization. After it has ... routine; evaluate P(t) by decoding routine; while (not terminating condition) do create C(t) fromP(t) by crossover routine ...
... routine, in each generation, (2) generate and (3) evaluate popSize ˇ pI random members, and (4) replace the popSizeˇp I worst members of the population with the popSizeˇp I random members (pI, called the immigration probability). The ...
... routine; evaluate P(t ) by decoding routine; while (not terminating condition) do 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 ...
... routine; calculate objectives fi (P),i = 1,ˇˇˇ,q by decoding routine; 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 ...
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 |