Particle swarm optimization (PSO) in recent years has been widely applied to solve various real world problems. However, for ill conditioned problems with largely different sensitivity to the objective function, classical PSO cannot search for optimal solution efficiently due to the best position-guided strategy that wastes lots of source searching undesirable areas. Therefore, this paper proposes a novel velocity reinforced mechanism (VR) for solving m-conditional problems. Two implementations of the mechanism, velocity reinforced particle swarm optimization and velocity reinforced search, are introduced in this paper. VR updates its velocity by learning and correcting best velocity directly, instead of using classical best position-guided updating rules. In this way, it increases the possibility that finds better directions for m-conditional problems. Experiments indicate that the novel approaches improve the final results and efficiency.