一种求解约束优化问题的新粒子群算法

A NEW PARTICLE SWARM ALGORITHM FOR SOLVING CONSTRAINED OPTIMIZATION

结合外点法具有局部搜索能力强、处理约束条件简单的特点,把违反约束的粒子用外点法处理以满足约束设计出一种新的粒子群算法求解约束优化问题。实验结果表明,新算法性能优于现有其它算法,是一种通用、高效、稳健的智能算法。它兼顾粒子群算法和外点法的优点,既有较快的收敛速度,又能以非常大概率求得约束优化问题的全局最优解,同时还提高了解的精度。

To keep those infeasible particles in feasible region, external point method is taken for its effective local search ability and simplicity of handling constrained conditions, a new particle swarm optimization algorithm is proposed for solving constrained opitmization problem. The experiment results demonstrate that the new particle swarm optimization algorithm is a general,effective and robust intelligent method, and its performance is superior to some other techniques. The proposed algorithm has paid attention to both the advantages of external point method and particle swarm optimization algorithm,it not only has a rather high convergence speed, but can also locate the global optimum with a rather high probability,and furthermore it improves the precision of solution.