Improved constrained optimization particle swarm optimization algorithm
一种改进的约束优化粒子群算法*

Using non-stationary multi-stage assignment penalty function method to deal with the constraint conditions, this paper proposed a novel constrained optimization particle swarm optimization algorithm. It used chaotic sequences in the initialization of the evolutionary population. In the process of population evolution, the proposed algorithm selected the best population individual for local search to speed up the convergence rate of the algorithm. It maintained the population diversity through dimension mutation method. Numerical experiment results show that it is an effective algorithm.