Hybrid Coevolutionary Particle Swarm Optimization for Linear Variational Inequality Problems

Solving linear variational inequality by traditional numerical iterative algorithm not only can not satisfy parallel, but also its precision has much relationship with initial values. In this paper, a novel hybrid coevolutionary particle swarm optimization is used to solve linear variational inequality, which sufficiently exerts the advantage of particle swarm optimization such as group search, global convergence and it satisfies the question of parallel solving linear variational inequality in engineering. It also overcomes the influence of initial values. Several numerical simulation results show that the coevolutionary algorithm offers an effective way to solve linear variational inequality, high convergence rate, high accuracy and robustness.