| 基于对标学习的智能优化算法 |
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| 引用本文： | 谢安世.基于对标学习的智能优化算法[J].电信科学,2018,34(7):92-101. |
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| 作者姓名： | 谢安世 |
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| 作者单位： | 浙江工业大学，浙江 杭州 310014 |
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| 基金项目： | 浙江省哲学社会科学重点研究基地技术创新与企业国际化研究中心项目;浙江工业大学中小微企业转型升级协同创新中心项目;教育部人文社会科学基金资助项目 |
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| 摘 要： | 科研、工程和管理中的很多问题都可以转化为优化问题。应用于这些优化问题的各种方法本身就是各种模型，设计不同的方法即设计不同的模型。将标杆管理理念建模成为一种用于单目标优化问题的元启发式搜索方法。基于奥卡姆剃刀原则，摒弃了复杂的操作算子的概率调优规则，用一个简单的框架来组织核心算子，从而达到许多组合算法的搜索效果。
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| 关 键 词： | 智能优化算法 探索性与开发性 全局搜索与局部优化 标杆管理 |
Intelligent optimization algorithm based on benchmarking |
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| Anshi XIE.Intelligent optimization algorithm based on benchmarking[J].Telecommunications Science,2018,34(7):92-101. |
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| Authors: | Anshi XIE |
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| Affiliation: | Zhejiang University of Technology,Hangzhou 310014,China |
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| Abstract: | Many of the issues in scientific research,engineeringand management can be transformed into optimization problems.The various methods applied to these problems were a variety of models.Designing different methods was designing different models.The theme was to model the benchmarking philosophy in business management as a meta-heuristic search method for single objective bound-constrained real-parameter optimization problems.According to the principle of Occam’s Razor,many complicated operators and their probability tuning rules were abandoned and a simple framework was used to organize the core operators to achieve the effect of many composition algorithms. |
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| Keywords: | intelligent optimization algorithm exploration and exploitation global search and local optimization benchmarking management |
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