Different from multiobjective optimization problems (MOPs), multimodal MOPs (MMOPs) focus on both decision and objective spaces rather than only objective one. Thus, finding a good Pareto front approximation and finding the maximal number of equivalent Pareto optimal solutions for each objective vector in the Pareto front are two core tasks for them. Although some multimodal multiobjective evolutionary algorithms have been proposed to handle them, they can quickly converge to the easy-to-find equivalent Pareto optimal solutions, thereby losing their ability to improve solution diversity in decision space and performance in objective space. To address the above issues, this work proposes a new information utilization method. Its core idea is to randomly extract a certain amount of decision variable information from the current optimal solutions to construct an information vector, which is, in turn, used to assist the generation of elite solutions. The proposed method can assist any available intelligent optimizers to improve their performance in solving MMOPs. This is confirmed by experimental results obtained from solving 22 such problems from CEC2019 and 12 scalable imbalanced distance minimization problems through a number of optimizers. Finally, we apply the proposed method to credit card fraud detection problems to show its practical significance.