平面选址问题实质上是带约束的非线性连续函数优化问题。
The essential of planar location issue is nonlinear continuous function optimization under constrained condition.
针对多极值连续函数优化问题，提出了一种自适应蚁群算法。
An adaptive ant colony algorithm is presented for the optimization of multi-minimum continuous function.
粒子群算法在求解连续函数优化问题中取得了广泛的应用，但是其在离散的组合优化问题中的运用还比较少。
Particle swarm optimization is widely used to solve continuous function optimization issue, but its application in discrete combination optimization problems is still relatively few.
求解连续函数优化问题在实际生产生活中有着十分重要的意义，是遗传算法的研究与应用的一个相当重要方向。
Continous function optimization is very important for us and is also an important research domain of genetic algorithm's research and its application.
相对于传统的概率分布估计算法，并行的概率分布估计算法在解决连续函数优化及实时优化问题时能提供极大程度的效率提高。
In contrast to traditional estimate distribution algorithm, parallel EDAs has greatly improved the efficiency when optimizing continuous functions and real time questions.
本文就此提出一种新的解决连续函数优化问题的连续型蚁群算法，它具有简单易实现且计算效率高等特点，特别适用于复杂工程优化问题。
Thus, such searches are typical optimizations of complicated continuous functions. A new method namely Continuous Ant Colony Algorithm is introduced to solve this problem.
该算法本质上是一种随机搜索算法，并能以较大概率收敛到全局最优，特别适用于连续函数的优化。
The algorithm is a random searching algorithm in nature. It can converge to the global minima more probability and be adept in continuous functions optimization.
微粒群优化算法是求解连续函数极值的一个有效方法。
Particle Swarm Optimization (PSO) algorithm is a powerful method to find the extremum of a continuous numerical function.
对于函数优化这个问题，根据目标函数定义域的性质，可以分为离散函数最优化和连续函数最优化。
For function optimization problems, according to the nature of the objective function domain, can be divided into discrete function optimization and continuous function optimization.
对于函数优化这个问题，根据目标函数定义域的性质，可以分为离散函数最优化和连续函数最优化。
For function optimization problems, according to the nature of the objective function domain, can be divided into discrete function optimization and continuous function optimization.
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