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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... the general implementation structure of GA is described as follows: procedure:basicGA input: problem data, GA parameters output: the best solution. 2 1 Multiobjective Genetic Algorithms GeneralStructureofaGeneticAlgorithm.
... input: problem data, GA parameters output: the best solution begin t <- 0; initialize P(t) by encoding routine; evaluate P(t ) by decoding routine; while (not terminating condition) do create C(t) fromP(f) by crossover routine; create C ...
... input: problem data, GA parameters output: Pareto optimal solutions E begin t ← 0; initialize P(t) by encoding routine; calculate objectives fi (P),i = 1,···,q by decoding routine; create Pareto E(P); evaluate eval(P) by fitness ...
... input : fitness value (), i evalv i popSize output rjq wr r = =∀∈ ∀∈ ←= ← ∑ begin [0,1], 1,2,..., ; //nonnegative random number j random q kk j j k () min 1 , 1,2,..., ; () () , ; (), ; q ikkik i kq eval w f v z i eval i ...
... input: the objective fk (v() of each chromosome vt, k = 1,2, ...,</, V/e popSize output: fitness value evo/(v(),Vi e popSize begin nondominated set P ' <— ^; dominated set P <— <jr, for i = 1 to popSize if v. is nondominated solution ...
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 |