Direct search feasible direction (DSFD) is an effective algorithm for solving the constrained non-linear programming problem, however, it could only find the local optimal solution.
转轴直径搜索可行方向法(DSFD方法)是一种比较有效的求解有约束非线性规划问题的算法，但它只能得到局部最优解。
Traditional optimization methods use much more information of the target problem, so their convergence speed is much better, and the ability of finding local optimal is better.
传统的优化方法充分利用了目标问题的信息，局部寻优能力较强，收敛速度较快，但又会陷入局部最优的陷阱。
Essentially, optimal reactive power dispatch is the multi-restraint overall optimization problem with plentiful local minima and discrete variables.
从本质上讲，无功优化调度问题是具有大量的局部极小值的多约束全局优化问题，且含有大量的离散变量。
These are all greedy which are static local optimal algorithm and have typical local optimization problem.
这些算法均属于贪心算法，存在典型的局部最小问题，是一种静态的局部最优算法。
Iterative TFIDF algorithm belongs to hill-climbing algorithm, it has the common problem of converging to local optimal value and sensitive to initial point.
迭代tfidf算法属于爬山算法，初始值的选取对精度影响较大，算法容易收敛到局部最优值。
Combining a heuristic random searching strategy with local optimal algorithms is effective solution for complex optimization problem.
启发式随机搜索策略和局部优化算法相结合的求解方案是解决复杂函数优化的有效途径。
An optimal apportionment of reliability problem of complex system is a nonlinear optimization problem with a large number of local extreme values.
复杂系统可靠度最优分配问题是一个具有多局部极值的非线性的优化问题。
GENIUS algorithm is used to solve TSP problem in this algorithm. Through this processing, not only better solution can be got, but also the likelihood of local optimal be reduced by perturbing sol...
采用GENIUS算法处理其中的TSP问题，不仅能产生较好的解，而且通过对解的周期性的扰动，进一步减少求解陷于局部优化的可能性。
But the algorithm easily trapped into local optimal solution and solved the problem more slowly, this paper constructed Max-Min ACO algorithm through the improvement and adjustment of ACO algorithm.
针对该算法易陷入局部最优解、求解速度较慢的缺陷，本文通过对蚁群算法的改进和调整，构造出最大—最小蚁群算法。
SVM has better generalization and guarantee the local optimal solution is exactly the global optimal solution. SVM can solve the learning problem of a smaller number of samples.
支持向量机具有小样本、较好的泛化能力、全局最优解等特点，在状态识别领域中表现出优良的特性。
SVM has better generalization and guarantee the local optimal solution is exactly the global optimal solution. SVM can solve the learning problem of a smaller number of samples.
支持向量机具有小样本、较好的泛化能力、全局最优解等特点，在状态识别领域中表现出优良的特性。
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