In this paper, an improved differential evolution algorithm and a new definition form of fitness function were proposed. 对差分演化算法进行了改进,给出了一种新的适应值函数的定义形式;
A method based on the Monte-Carlo simulation and differential evolution algorithm is used for solving the established optimization problem. 发展了蒙特卡罗模拟和差异进化(DE)算法相结合的方法来求解这一优化问题。
This paper mainly integrated ant colony algorithm, genetic operation and differential evolution algorithm's advantages. 主要综合了蚁群算法、遗传算法、差异演化算法三者优点。
A binary differential evolution algorithm with dual subpopulations algorithm for discrete optimization problems is proposed. 提出了一种求解离散优化问题的双种群二进制微分进化算法。
The optimization problem was solved by differential evolution and the constraints were handled by feasibility-based rule. 利用差分进化算法求解该优化问题,并利用可行性规则处理约束。