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.
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.
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.
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.
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.
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.
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).
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).
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