基于参数方程处理等式约束优化的粒子群算法

Particle swarm optimization algorithm based on parametric equation method to handle equality constraints

针对目前已有的粒子群优化算法求解有等式约束优化问题时对收敛速度和解的精度的影响,提出了一种新的基于参数方程的粒子群优化算法。它是粒子群在初始化和迭代进化过程中使用求解参数方程的方法处理等式约束设计出的粒子群优化算法。数值实验结果表明,新算法是有效的。它不仅提高了收敛速度和解的精度,而且是一种通用的智能算法。

To improve the speed of convergence and the precision for most of current particle swarm optimization algorithms being used to solve equality-constrained optimization problems, a new particle swarm optimization algorithm based on parametric equation method is presented. Parametric equation method is taken to keep particles satisfying with equality constraints during the process of population initiation and evolution, and a new particle swarm optimization algorithm is proposed. The experimental results demonstrate that the new particle swarm optimization algorithm is effective. The proposed algorithm not only improves performance of the speed of convergence and the precision, but also is a general, effective and robust method.