一种改进的多学习教与学优化算法

针对传统教与学算法在解决复杂多峰函数优化问题时,具有局部最优且搜索开发能力较差的缺点,提出了一种改进的多学习教与学优化算法,新算法为学员的每一维加入不同的教学因子,设计了基于学员均值比较的教师选择策略和向教师及学员学习的多学习策略。基于多个单峰和多峰函数的仿真结果表明,新算法跟传统的、改进的教与学算法相比,在稳定性、寻优精度和收敛速度方面更具优势。     

美国新潮:通过明星的嘴和脸来环保

在纽约,人们不得不尝试采取各种形式来推进环境保护工作的进程,八仙过海各显其能.去年7月4日华盛顿庆祝国庆节的游行中,我就亲眼看到一个宣传环保的队伍,队中的每个人都拿着一个垃圾袋,边走边收集游行者和观众丢弃的易拉罐,成为国庆节游行的一个亮点.     

Trends in management plans and guides: 25 years of experience from Southern France

This study analysed 14 management plans and guidelines from a 25-year period to understand trends in conservation planning. A Rosetta Stone Analysis was used for the systematic comparison of plans and guidelines. Management plans incorporated management philosophies, management scenarios, opportunities for infrastructure, and plans for data collection by 2000. As of 2010, they incorporated ecosystem services, stakeholders’ objectives and methods for storing and analysing data. The results demonstrate the complex nature of management plans, with an important workload for site managers. Recommendations for future planning include adjustments in planning timeframes and a better identification of conservation targets from initial stages.     

Development and Operational Testing of a Super‐Ensemble Artificial Intelligence Flood‐Forecast Model for a Pacific Northwest River

Coastal catchments in British Columbia, Canada, experience a complex mixture of rainfall‐ and snowmelt‐driven contributions to flood events. Few operational flood‐forecast models are available in the region. Here, we integrated a number of proven technologies in a novel way to produce a super‐ensemble forecast system for the Englishman River, a flood‐prone stream on Vancouver Island. This three‐day‐ahead modeling system utilizes up to 42 numerical weather prediction model outputs from the North American Ensemble Forecast System, combined with six artificial neural network‐based streamflow models representing various slightly different system conceptualizations, all of which were trained exclusively on historical high‐flow data. As such, the system combines relatively low model development times and costs with the generation of fully probabilistic forecasts reflecting uncertainty in the simulation of both atmospheric and terrestrial hydrologic dynamics. Results from operational testing by British Columbia's flood forecasting agency during the 2013‐2014 storm season suggest that the prediction system is operationally useful and robust.