这种算法结合了启发式算法和随机化算法以及局部寻优的思想。
The proposed algorithm incorporates the thoughts of heuristic algorithm, randomized algorithm and local optimization.
算法引入模拟退火机制，在遗传进化过程中的每一代，对最优个体进行邻域局部寻优，利用模拟退火进一步改善算法的收敛性能。
Simulated annealing mechanism is introduced to do local-search for the best chromosome in every generation of the evolution process. This improves the convergence of the algorithm.
本文通过引入局部增强算子，使种群中的部分个体在当前最优个体附近寻优，以加快算法的收敛速度。
To improve the efficiency of the algorithm, the local enhanced operator is proposed to make some individuals of the population search around the current best individual.
该优化算法具有混沌优化的全局搜索能力，而模式搜索法则加快了局部寻优性能。
The proposed hybrid algorithm had the global search ability of chaotic search as well as local search ability of pattern search method.
基于混沌序列的多峰函数微粒群寻优算法的目标就是找到多峰函数的所有局部优化峰值。
It makes a searching for all local optimization of the multimodal function that a PSO algorithm based on chaos sequence for multi-modal function optimization.
该算法既通过压缩寻优空间提高了遗传算法的搜索效率，又利用改进最优流法改善了局部寻优能力。
The proposed algorithm could enhance the search efficiency of GA by compressing the search space and improve the local search ability by use of improved optima flow pattern algorithm.
通过设定逃逸系数，算法在寻优过程中具有了能够跳出局部极小点到达全局最优点的能力。
In the process of optimization, the method has the ability of escaping from the local minimized point and arriving at the global optimal point by setting an escaping coefficient.
聚类方式的改进不仅增加了算法的局部寻优能力，有效地减少了计算的复杂度，而且还具备一定的野值剔除能力。
The improvement of clustering can strengthen the algorithms local optimization capability and reduce the computation complexity considerably while eliminating some outliers.
最后，结合IBP算法强大的局部寻优能力完成整个温室小气候模型的建立。
Finally, the whole microclimate model in greenhouse was set up with the powerful local-optimization of IBP algorithm.
但遗传算法的局部寻优能力不足，因此得到的结果在精度上也受到了限制。
But there are also limitations in local searching ability of the algorithm, so as for the precision of searching results.
实验结果证明，与传统PSO算法相比，改进算法的寻优效果较好，可在一定程度上避免陷入局部最优。
Experimental results show that the improved algorithm performs better than the traditional PSO and may avoid falling into the local optimum instead.
针对粒子群算法用于高维数、多局部极值点的复杂函数寻优时易陷入局部最优解现象，提出一种改进的带扰动项粒子群算法并进行收敛性分析。
Traditional particle swarm optimization(PSO) algorithms often trap into local minima easily when used for the optimization of high-dimensional complex functions with a lot of local minima.
针对粒子群优化(PSO)算法在寻优时容易陷入局部最优的不足，提出一种基于子区域的PSO算法。
Aiming at problem that Particle Swarm Optimization (PSO) algorithm falls into local optimum easily, this paper presents a PSO algorithm based on sub-region.
针对粒子群优化(PSO)算法在寻优时容易陷入局部最优的不足，提出一种基于子区域的PSO算法。
Aiming at problem that Particle Swarm Optimization (PSO) algorithm falls into local optimum easily, this paper presents a PSO algorithm based on sub-region.
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