在此基础上，进一步提出了混合粒子群算法和速度变异粒子群算法两种改进算法提高优化性能。
For improving the predicting results, two improved PSO algorithm are presented also in this paper: Velocity Mutation PSO and hybrid PSO.
该文针对机组组合问题，提出了一种新的混合粒子群优化算法。
This paper proposes a new hybrid particle swarm optimization method for unit commitment problem.
提出了一种新颖的基于粒子群优化和多级检测的混合算法的多用户检测器。
A novel hybrid algorithm approach that employs a particle swarm optimization (PSO) and a multistage detection for the multiuser detection problem (PSOMSD) is proposed.
基于粒子群算法运用随机模拟和模糊模拟相结合的技术，给出了一种求解该规划模型的混合智能算法。
Desgined a mixed intelligent arithmetic by using the technique of combing the stochastic simulation with fuzzy simulation and particle swarm optimization algorithm to solve this kind of problems.
为了提高优化性能，将粒子群算法和模拟退火算法结合，得到了粒子群-模拟退火混合调度算法。
In order to improve its performance, the paper puts forward a hybrid algorithm which blends the PSO algorithm and simulated annealing algorithm.
与其他混合最优化算法不同的是，该算法没有破坏粒子群和遗传算法的独立性，而是仅通过全局最优样本把两个算法结合在一起。
It selects the optimaler number as a global optimum at every circulation, which makes its result be better than both PSO and GA, then improves the overall performance of the algorithm.
针对模糊c均值算法与粒子群算法的不足，提出了一种基于粒子群算法和模糊c—均值算法的混合聚类算法。
To avoid the shortcomings of FCM and Particle Swarm Optimization algorithm, new hybrid clustering algorithm based on PSO and FCM algorithm is proposed.
文章将量子进化算法(QEA)和粒子群算法(PSO)互相结合，提出了两种混合量子进化算法。
Inspired by the idea of hybrid optimization algorithms, this paper proposes two hybrid Quantum Evolutionary algorithms (QEA) based on combining QEA with Particle Swarm optimization (PSO).
应用粒子群优化算法(PSO)求解电力系统无功优化问题，提出基于混沌搜索的混合粒子群优化算法，以克服P SO容易早熟而陷入局部最优解的缺点。
The chaos search based hybrid particle swarm optimization (PSO) algorithm is proposed in the paper to avoid the premature phenomenon of PSO, which is applied into the reactive power optimization.
我们也建议用混合版本的粒子群算法嵌入局部优化，提高了性能。
We also propose to use a hybrid version of PSO embedding a local optimizer to enhance the performance.
本文将基于模拟退火的粒子群优化算法这一混合优化算法应用于拆卸序列规划求解问题。
It be used in disassembly sequence planning process which is a hybrid optimization algorithm, the particle swarm-simulated annealing optimization algorithm (PSO-SA).
为了提高粒子群算法的寻优速度和寻优精度，提出一种改进的PSO混合算法。
This paper proposed a novel Particle Swarm Optimization (PSO) hybrid algorithm to improve the optimum speed and performance of the PSO algorithm.
采用峰度作为适应度函数，利用粒子群算法对由多个源信号混合而成的信号进行盲抽取。
In the method, peak is used as fitness function, and the PSO algorithm is used to withdraw blindly from several signals.
针对0-1 背包问题，提出一种具有修复策略的、贪心算法与二进制粒子群算法相结合的混合智能算法。
The simulation result indicates that the performance of BSPSO on knapsack problem, with a quicker convergence, is superior to the greed and genetic algorithms.
最后，对所给的粒子群遗传混合算法，在电子商务VMART系统进行相关的实验。
At last, HPSOGA is implemented in ane-commerce simulation system, namingly VMART.
最后，对所给的粒子群遗传混合算法，在电子商务VMART系统进行相关的实验。
At last, HPSOGA is implemented in ane-commerce simulation system, namingly VMART.
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