查询词典 optimization

Particle Swarm Optimization is adopted to detect parameter pattern e.g. circle, ellipse, hyperbola and asymptote.

我们采用一种以粒子群演算法Particle Swarm Optimization 为基础的方法，用来侦测一张影像上的参数图形(如：圆、椭圆、双曲线与双曲线的渐近线)的参数。

This paper applies Particle Swarm Optimization algorithm in the cryptology area to design Boolean function in cryptography.

将粒子群(Particle Swarm Optimization，PSO)算法应用于密码学领域，设计密码学中的密码函数—布尔函数。

Conic optimization is a particular case of convex programming, and it is also an extension of linear programming.

锥规划(conic optimization，简称CO)是一种特殊的凸规划，是线性规划的推广。

This paper presents a new data clustering algorithm based on glowworm swarm optimization algorithm.

摘要 本研究以萤火虫最佳化(Glowworm Swarm Optimization， GSO)演算法为基础，发展出新的资料分群方法，我们称之为CGSO。

In: Proceedings of the 1999 Congress on Evolutionary Computation, Piscataway, NJ: IEEE Press, 1999: 1958~1962.[9] Kennedy J. Small worlds and Mega-minds: effects of neighborhood topology on particle swarm performance. In: Proceedings Congress on Evolutionary Computation, Piscataway, NJ: IEEE Press, 1999: 1931~1938.[10] Peram T, Veeramachaneni K, Mohan C K. Fitness-distance-ratio based particle swarm optimization.

近来性能较为显著的基于PSO算法的改进算法有Thanmaya Peram[10]等人于2003年提出的基于粒子群优化的适应值-距离-比例算法(Fitness-Distance-Ratio based Particle Swarm Optimization， FDR-PSO)，在此算法中每个粒子根据一定的适应值-距离-比例原则，向附近具有较好适应值的多个粒子进行不同程度的靠近，而不仅仅只向当前所发现的最好粒子靠近。

The main innovations of this paper are listed as follows.(1) The ant colony optimization method is studied and designed for a single batch-processing machine with non-identical job sizes.

本文的主要工作和创新点如下：(1)研究了蚁群算法(Ant Colony Optimization，ACO)在差异工件单机批调度问题中的应用。

So this paper puts forward an Energy Balance Data Query Protocol based on Ant Colony Optimization. This protocol rewards or punishes a path according to the energy consumption by pheromone decentralize energy consumption into different paths, and reposefully demotes the energy consumption of the whole network.

因此，提出一种基于蚁群优化的能量均衡数据查询协议EBDQ(energy balance data query protocol based on ant colony optimization)，根据路径上的能量消耗情况，通过信息素对其进行奖惩，使网络的能量消耗分散在不同的路径上，让整个网络的能量平稳降级。

To avoid the prematurity phenomenon of particle swarm optimization, a novel hybrid genetic particle swarm optimization algorithm is proposed. An eliminative mechanism is introduced into the iteration of PSO.

针对粒子群算法容易出现&早熟&的缺点，提出了一种改进的混合遗传粒子群(hybrid genetic particle swarm optimization， HGPSO)算法。

This paper proposes an Improved Particle Swarm Optimization algorithm.IPSO adopts a new mutation operator and a new method that congregates some neighboring individuals to form multiple sub-populations in order to lead particles to explore new search space.Additionally,this algorithm incorporates a mechanism with a simple and easy penalty function to handle constraint.Thus,this algorithm has strong global exploratory capability and efficiency while being applied to solve nonlinear constrained optimization problems.

提出了一种改进的粒子群算法（Improved Particle Swarm Optimization，IPSO），使用了一种新型的变异策略，并在搜索过程中将部分邻近的个体聚集成核，从而形成多子群引导粒子探测新的搜索区域，采用了简单易行的罚函数约束处理机制，使算法在求解较难的非线性约束优化问题时具有很强的全局搜索能力与效率。

Firstly, on the basis of defining the characteristics of project and reviewing the mathematical models for RCPSP, we constructed the mathematical model for RCPSP under the condition that there is non-fixed duration and splittable tasks in project; In order to solve above model, we emphatically discussed the basic principle of the swarm intelligence algorithm--the particle swarm optimization algorithm as well as improvement research for current algorithm, and then proposed a new improved particle swarm optimization algorithm and analyzed its application process in the resource-constrained project scheduling problem; Lastly, in order to confirm the validity of the model and the algorithm, we took an experiment based on 108 instances that produced by problem instance generator which was developed by our team.

首先，在对RCPSP项目特征进行界定以及对项目进度优化模型回顾的基础之上，构建了基于活动工期不固定、任务可拆分特征的RCPSP的数学模型；为了求解上述模型，本文详细阐述粒子群优化算法(Particle Swarm Optimization，PSO)的基本原理以及当前对其改进的研究成果，提出了一种新的改进PSO算法，并对其在RCPSP的应用过程进行了设计；最后，为了检验模型的有效性及算法的优越性，基于梯队内部开发的案例生成器生成的108个算例，进行了仿真试验。

The split between the two groups can hardly be papered over.

这两个团体间的分歧难以掩饰。

This approach not only encourages a greater number of responses, but minimizes the likelihood of stale groupthink.

这种做法不仅鼓励了更多的反应，而且减少跟风的可能性。