The performance of DE algorithm is superior to other algorithm in numerical function optimization.
其中，差分演化算法在数值函数优化方面的性能要优于其它的优化算法。
The classical Particle swarm optimization (PSO) algorithm is a powerful method to find the minimum of a numerical function, on a continuous definition domain.
经典的粒子群优化算法是一个在连续的定义域内搜索数值函数极值的很有效的方法。
Particle Swarm Optimization (PSO) algorithm is a powerful method to find the extremum of a continuous numerical function.
微粒群优化算法是求解连续函数极值的一个有效方法。
The proposed adaptive optimization method has been validated using several analytic function tests. Numerical results show that the algorithm has the property of fast global searching.
利用解析函数对上述自适应优化方法进行了验证，算例结果证明了该算法的全局搜索和快速寻优能力。
Numerical results show that present fitness function is better than that constructed by linear weighted combination and that 3d optimization using genetic algorithms has to be paralleled.
算例表明：使用的适应函数优于传统线性组合法构成的，遗传算法计算三维优化问题时必须并行化。
Numerical results show that present fitness function is better than that constructed by linear weighted combination and that 3d optimization using genetic algorithms has to be paralleled.
算例表明：使用的适应函数优于传统线性组合法构成的，遗传算法计算三维优化问题时必须并行化。
应用推荐