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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... popSize). This probability controls the expected number pC × popSize of chromosomes to undergo the crossover operation. A higher crossover probability allows exploration of more of the solution space, and reduces the chances of settling ...
... popSize·p I worst members of the population with the popSize·p I random members (pI, called the immigration probability). The probabilities of immigration and crossover determine the weight of exploitation for the search space. The main ...
... . . . q generation t parents generationt+1 1 ... . . . . . . 1 1 ... . . . . . . step 1 2 step 2 Shuffle step 3 Selectn subgroups using each objective in turn ... . . . Apply genetic operators popSize q popSize z 1 2 □ 2 ◇ 3 △ △ 2.
... popSize 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 ...
... 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 then P'<-PVj{v,}; else P<-Pu)vj; JV<-|P| «,<-|S|, Q={j\i>-JJeP}, VieP'; s ...
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 |