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Research and Application of Multi-objective Genetic Algorithm Based on Improved Banker Law and Clustering

Author: XiaoYan
Tutor: XuFeng
School: Anhui University of Technology
Course: Applied Computer Technology
Keywords: Multi - objective Genetic Algorithms Makers law Clustering method Flow Shop Scheduling Water supply system
CLC: TP18
Type: Master's thesis
Year: 2011
Downloads: 20
Quote: 0
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Abstract


Genetic algorithm was originally proposed by the United States Holland , after Goldberg summarizes draw the basic frame structure . It can solve many problems in real life , such as production scheduling problems , combinatorial optimization , function optimization , image processing , robotics , machine learning , automatic control . Genetic algorithms in the processing of single objective problem has been a very good application , but the reality of the many issues that need to meet multiple objectives at the same time , and therefore , there has been a multi - objective genetic algorithm . Pareto - based multi-objective genetic algorithm is a research focus , its main feature is Pareto optimal selection mechanism into . The Makers law is one of the efficient way to construct Pareto optimal solution set . Clustering is one of the better methods to maintain the diversity of the population the , and bookmakers rule combined with better operating results and operating efficiency of the optimization algorithm . The main contents include the following aspects : 1 . Chapter a brief introduction genetic algorithm , multi- objective optimization problem , based on the Pareto multi- objective genetic algorithm , and multi-objective Genetic Algorithm Research and the research status . 2 The second chapter introduces the concept of Pareto optimal set and Pareto optimal boundary well Makers basic idea of the rule , and described in detail by way of example, the non- dominating set constructed Makers rule . Chapter Makers rule the improvements in the the original bookmaker rule based on a Vice Makers to participate in each round of comparison , the number of rounds to reduce the comparison . And compared with the unmodified bookmaker rule proved by experiment, the efficiency of the improved rules bookmaker . Clustering methods were introduced , and the clustering method shows the optimal solution . Chapter improved Makers rule and clustering method applied to the flow shop scheduling problem and the water supply system . Algorithm using clustering methods to maintain the evolving population diversity , non- dominating set Makers rule structure improved . The experiments show that the improved rules bookmaker and the combination of clustering method can achieve better optimization results .

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CLC: > Industrial Technology > Automation technology,computer technology > Automated basic theory > Artificial intelligence theory
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