国外新型智能优化算法--北极熊算法  

A Newly Proposed Intelligent Optimization Algorithm--Polar Bear Optimization

作　　者：张亮[1] 韩毅 邓丽丽[1] Zhang Liang;Han Yi;Deng Lili(College of Management,Zhejiang University of Technology,Hangzhou 310023,China;College of Fundamental Education,Zhejiang Shuren University,Hangzhou 312028,China)

机构地区：[1]浙江工业大学管理学院,杭州310023 [2]浙江树人大学基础学院,浙江绍兴312028

出　　处：《计算机测量与控制》2020年第3期212-217,241,共7页Computer Measurement &Control

基　　金：国家自然科学基金(71301147);教育部人文社科青年基金(12YJCZH065)。

摘　　要：北极熊算法(Polar Bear Optimization,PBO)是2017年由David等人提出的一种受自然界启发的优化算法,算法的灵感来自于北极熊赖以在北极严酷的环境下生存下来的捕猎方式;由于PBO是近年才提出来的新颖智能优化算法,中文文献中关于PBO算法的描述和应用微乎其微;还原了PBO的开发背景,介绍了算法的相关运算算子和算法的详细执行步骤,展现了PBO算法在现实世界中的应用领域和实际效果。Polar Bear Optimization(PBO)is a nature-inspired optimization algorithm proposed by David,et al.in 2017.The inspiration for PBO comes from the hunting styles that Polar bears rely on to survive in the harsh arctic environment.PBO is a novel intelligent optimization algorithm presented only in recent years.Therefore,there are few descriptions and applications of PBO algorithm in openly published Chinese literature.In this paper,the development background of PBO is repeated and the relevant operators and detailed executive steps are introduced.Finally,the application fields and practical effects of PBO in real world are well displayed.

关 键 词：北极熊算法 智能优化 算子 局部搜索 全局搜索 

分 类 号：TP39[自动化与计算机技术—计算机应用技术]