关于在“最优化方法”中引入智能优化算法的思考

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英文篇名：Comments on Introducing Intelligent Optimization Algorithms to "Optimization Methods" 作者：孙靖 ; 查明明 英文作者：Sun Jing;Zha Mingming; 关键词：最优化方法 ; 智能优化算法 ; 遗传算法 ; 人工智能 英文关键词：optimization methods;;intelligent optimization algorithm;;genetic algorithm;;artificial intelligence 中文刊名：KJWH 英文刊名：The Science Education Article Collects 机构：淮海工学院; 出版日期：2018-10-10 出版单位：科教文汇(上旬刊) 年：2018 期：No.436 语种：中文; 页：KJWH201810024 页数：2 CN：10 ISSN：34-1274/G 分类号：59-60

摘要

最优化方法是数学专业的重要课程,也是理工科专业必备的数学工具,通常仅限于数值优化方法。由于传统数值优化方法对目标函数要求较高,因此,难以解决实际复杂优化问题。本文从引入智能优化算法的必要性、智能优化算法选讲,以及数值优化方法和智能优化算法的融合等3个方面提出几点思考,以期推动"最优化方法"课程体系的改革,使其契合科学技术发展的需求。
        Optimization methods is one of the main courses in mathematics and a requisite mathematical tool for science and engineering majors. They are usually confined to numerical optimization methods. Due to the high requirements of traditional numerical optimization methods to objective functions, they cannot deal with practical complex optimization problems. In this paper,several comments are put forward from three aspects, i.e., the necessity of introducing intelligent optimization algorithms, selected introductions of intelligent optimization algorithms and the fusion of numerical and intelligent optimization algorithms. It is expected to boost the curriculum reform of optimization methods and meet the demand of scientific and technological development.

引文

[1]马昌凤.最优化方法及其Matlab程序设计[M].北京:科学出版社,2010.
    [2]吕聪颖.智能优化方法的研究及应用[M].北京:中国水利水电出版社,2014.
    [3]Sindhya K.,Miettinen K.,Deb K.A hybrid framework for evolutionary multi-objective optimization[J].IEEE Transactions on Evolutionary Computation,2013,17(4):495-511.
    [4]Ke L.,Guo H.,Zhang Q.A cooperative approach between metaheuristic and branch-and-price for the team orienteering problem with time windows[C].IEEE Congress on Evolutionary Computation.New York:IEEE Press,2014:1878-1882.