Solving Linear Variation Inequality by Particle Swarm Optimization
Liangdong Qu, Dengxu He · 2010
Solving linear variation inequality by traditional numerical iterative algorithm can not satisfy parallel. In this paper, particle swarm optimization is used to solve linear variation inequality, which sufficiently exerts the advantage of particle swarm optimization such as group search and global convergence and it satisfies the question of parallel solving linear variation inequality in engineering. Several numerical simulation results show that the algorithm offers an effective way to solve linear variation inequality, high convergence rate, high accuracy and robustness.