国外新型智能优化算法——北极熊算法

A newly proposed intelligent optimization algorithm--Polar bear optimization

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。

Abstract:

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.