As heat exchange loads are treated as continuous variables to be optimized in heat exchanger network (HEN) problems, temperature cross could be generated in stream matches. Infeasible structures with temperature cross are penalized by penalty function method in literature. However, in random walk algorithm with compulsive evolution (RWCE) algorithm to HEN optimization, structures with temperature cross may be accepted as imperfect solutions due to its mutation operation of accepting imperfect solutions, which may negatively affect optimization process and decrease algorithm efficiency. Hence, temperature cross in HEN is firstly explained. Then negative effects of infeasible structures on optimization process of RWCE is described. A treatment method is proposed: Individuals trapped in infeasible regions are compulsively returned to their original positions in feasible regions to accept re-optimization. It was demonstrated that the proposed method improves algorithm efficiency and achieves better results than reported ones in literature.