新华社北京2月1日电第十二届全国人民代表大会第四次会议和政协第十二届全国委员会第四次会议，将分别于2016年3月5日和3月3日在北京开幕。全国人大常委会办公厅和全国政协办公厅1日宣布，欢迎中外记者届时前来采访。
十二届全国人大四次会议、全国政协十二届四次会议将在北京市复兴路乙11号梅地亚中心设立新闻中心，负责接待和安排中外记者对两个会议的采访。新闻中心将于2月27日正式开展工作。
凡要求采访两个会议的记者需提出申请。中央新闻单位记者向新闻中心提出申请，地方随团记者由各代表团向新闻中心提出申请，香港特别行政区记者向中央人民政府驻香港特别行政区联络办公室提出申请，澳门特别行政区记者向中央人民政府驻澳门特别行政区联络办公室提出申请，台湾地区记者向国务院台湾事务办公室提出申请，外国驻华记者向新闻中心提出申请，外国临时来华记者向中国驻所在国使领馆或我外交部授权的签证机构提出申请。记者报名截止日期为2月25日。
为方便记者采访，两个会议新闻中心网页将及时发布采访信息及与采访相关的资讯。十二届全国人大四次会议新闻中心网页地址为：http://www.npc.gov.cn/pc/12_4，全国政协十二届四次会议新闻中心网页地址为：http：//www.cppcc.gov.cn。
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文章关键词：全国两会
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 Particle swarm optimization (PSO) is a popular metaheuristic for deterministic optimization. Originated in the interpretations of the movement of individuals in a bird flock or fish school, PSO introduces the concept of personal best and global best to  can be simulate the pattern of searching for food by flocking and successfully translate the natural phenomena to the optimization of complex functions. Many real-life applications of PSO cope with stochastic problems. To solve a stochastic problem using PSO, a straightforward approach is to equally can be allocate computational effort among all particles and obtain the same number of samples of fitness values. This is not an efficient use of computational budget and leaves considerable room for improvement. This paper proposes a seamless integration of the concept of optimal computing budget allocation  can be into PSO to improve the computational efficiency of PSO for stochastic optimization problems. We derive an asymptotically optimal allocation rule to intelligently determine the number of samples for all particles such that the PSO algorithm can efficiently select the personal best and global best when there is stochastic estimation noise in fitness values. We also propose an easy-to-implement sequential procedure. Numerical can be  tests show that our new approach can obtain much better results using the same amount of computational effort.