For Multi-objective Optimization Problem （MOP）， the goal is to obtain a well-distributed set of Pareto optimal solutions. When the variables are constrained， the Pareto solutions are typically located in different feasible regions. For large-scale and tightly constrained optimization problems， the existing evolutionary algorithms have difficulty in balancing diversity and convergence of individuals， resulting in the search population may not being able to cross multiple infeasible regions and then converging prematurely. To address this issue， an effective Information-guided Adaptive Constrained Multi-objective Evolutionary Algorithm （IACMEA） was proposed. First， a co-evolutionary mode of main and auxiliary populations was adopted： the main population mainly searched within the feasible regions and converged to the Pareto fronts， and the auxiliary population retained infeasible solutions with small objective function values through environmental selection based on the constraint threshold， thereby maintaining population diversity. Second， to improve the quality of individuals in the populations， a mating pool selection strategy was designed on the basis of the feasible solution ratio. Experimental results on the CF， LIRCMOP， and DASCMOP benchmark test sets show that， comparing IACMEA with six similar algorithms including CCMO （Coevolutionary Constrained Multi-objective Optimization framework）， IACMEA achieves 23 best Inverted Generational Distance （IGD） values and 22 best HyperVolume （HV） values out of 33 problems. It can be observed that IACMEA demonstrates certain advantages in handling nonlinear Constrained Multi-objective Optimization Problems （CMOPs）.

The traditional graph-based recommendation algorithm neglects the combined time factor which results in the poor recommendation quality. In order to solve this problem, a personalized recommendation algorithm integrating roulette walk and combined time effect was proposed. Based on the user-item bipartite graph, the algorithm introduced attenuation function to quantize combined time factor as association probability of the nodes; Then roulette selection model was utilized to select the next target node according to those associated probability of the nodes skillfully; Finally, the top-N recommendation for each user was provided. The experimental results show that the improved algorithm is better in terms of precision, recall and coverage index, compared with the conventional PersonalRank random-walk algorithm.