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基于并行遗传算法的多目标优化问题研究

Abstract第4-5页
摘要第6-10页
1 Introduction第10-14页
    1.1 Motivation第10-11页
    1.2 Literature review第11-13页
        1.2.1 History of the question第11-12页
        1.2.2 Modern approaches第12-13页
    1.3 Thesis Outline/Organization第13-14页
2 Analysis of methods and algorithms of multi-objective optimization problem and resolving the research task第14-30页
    2.1 Analysis of the main provisions of multi-objective optimization第14-15页
    2.2 Domination and effectiveness. Speaking of criterial vectors, consider two types of domination第15-17页
    2.3 Parallel genetics algorithms in data clustering methods第17-22页
        2.3.1 A clustering method based on parallel genetic algorithm第18-22页
    2.4 Analysis of multi-objective optimization methods. An evolutionary approach第22-24页
    2.5 General description of the genetic algorithm第24-26页
    2.6 Analysis of the effectiveness of known genetic multi-objective optimization algorithms第26-28页
    2.7 Conclusions and formulations of research tasks第28-30页
3 Development of the parallel genetic algorithm for solving task of multi-objective optimization第30-42页
    3.1 General features of parallel genetic and evolutionary algorithms第30-32页
    3.2 Development of an experimental scheme for the exchange of genetic information ..第32-34页
    3.3 Development of advanced genetic algorithm第34-35页
    3.4 Leaders selection第35-37页
    3.5 Development of algorithm for solution multi-objective optimization problem第37-38页
    3.6 The choice of test functions第38-40页
    3.7 Development of research plan第40-42页
4 Program realization and investigation of the genetic algorithm properties第42-49页
    4.1 Development of a program for studying the properties of a genetic algorithm第42-43页
    4.2 Results of the study of parallel genetic algorithm第43-45页
    4.3 Development of a program that implements the solution of the multi-objective optimization problem第45-49页
5 Conclusions第49-50页
Acknowledgements第50-51页
Abbreviations第51-52页
References第52-55页
Publications第55页

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