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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... input : fitness value (), i evalv i popSize output zfvkq z begin max { } { ()}, 1,2,..., ; //maximum extreme point kki i + max =∀∈ ∀∈ ←= min {} k z ← m { ()}, 1,2,...,; //minimum extreme point ki i fv k q z in wkq zz () 1 , 1,2 ...
... input: the objective/; (v,) of 42 1 Multiobjective Genetic Algorithms. 0 2 4 6 8 10 12 024681012 0 2 4 6 8 10 12 024681012 0 2 4 6 8 10 12 024681012 z2 0 2 4 6 8 10 12 024681012 0 2 4 6 8 10 12 024681012 0 2 4 6 8 10 12 024681012 z2 11 ...
... input: network data (N, A, 44 1 Multiobjective Genetic Algorithms. z 2 □ △ |S j| △ □ △ □:reference solution r △: current solutionj 1z □□ □ □ □ □ □ □ △ △ △△△△ △ □ △ i 11122233345666677889910 j ...
... input: network data (N, A, c, d), GA parameters (popSize, maxGen, pM, pC, pI) output: Pareto optimal solutions E begin t ← 0; initialize P(t) by priority-based encoding routine; calculate objectives zi (P),i = 1,···,q by priority-based ...
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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 |