全局极值 meaning in Chinese
globales extremum global extremum
Examples
- Pso is simple and efficient , so many researchers have been attracted by this algorithm , and furthermore , it converges fast by moving each particle aimed at guides when it deals with single - objective optimization , and these features are important in multi - objective optimization also . from some current research works , we describe a multi - objective particle swarm optimization algorithm ( mopso ) that incorporates the concept of the enhanced - dominance , we present this new concept to update the archive , the archiving technique can help us to maintain a sequence of well - spread solutions . a new particle update strategy and the mutation operator are shown to speed up convergence
目前,国内外已有部分相关研究成果,但是它们在解集分布性、收敛性方面仍存在不足,在吸取已有成果的基础上,本文提出了一种改进的多目标粒子群算法( mopso ) ,使用我们提出的强支配概念构造外部种群,使解集保持良好的分布性,同时,通过采用新的全局极值和个体极值的选取方式及采用新的种群更新策略加快解集的收敛,提出基于快速排序的非支配集构造方法加快算法运行效率。 - Then based on the idea of predictive motion vector , using of spatial correlation of adjacent block and global minimum points probability distribution characteristic , predictive diamond searching ( pds ) and its advanced mode : adaptive pds ( apds ) are introduced . finally the algorithm of pds and apds and its simulation results comparing with conventional me algorithm are given
然后基于预测性运动矢量的概念，利用相邻块运动矢量的相关性和全局极值点概率分布特性，提出了预测性菱形搜索算法和它的改进算法：自适应预测性菱形搜索法，设计出具体算法，并给出了与传统快速块匹配法比较的计算机仿真结果。 - Both of them efficiently improve the performance of basic pso , but they still have the weakness of premature convergence when they are used in multi - dimension problems . further , an improved algorithm is proposed using the characteristics of the flight of geese for reference . the improved algorithm has superiority over pso ; for one thing , it keeps the population various by ordering all particles and making each particle fly following its anterior particle ; for another thing , it strengthens cooperation and competition between particles by making each particle share more useful information of the other particles
针对上述缺点,本文借鉴生物界中雁群的飞行特征,对两种标准算法均给出如下改进:一方面将全局极值变换为排序后其前面那个较优粒子的个体极值,这样所有粒子不都向一个最优解的方向飞去,避免了同一化,保持了多样性,扩大了搜索范围;另一方面使每个粒子利用更多其他粒子的有用信息,通过个体极值加权平均,加强粒子之间的合作与竞争。