To address the scheduling problem of multi-department cooperative treatment of patients under the condition of limited medical resources in a hospital, this paper proposes a collaborative treatment scheduling algorithm based on intelligent optimization. The proposed algorithm regards the cooperative treatment scheduling of doctors, nurses and patients in different scenarios as a multi-role cooperative control problem. In order to optimize the role's access behavior, we propose a decision-making model to guide the role to make the optimal access behavior, and introduce an intelligent optimization algorithm to optimize the decision-making model. For the case scenarios of collaborative treatment of patients, doctor ward rounds, and patient physical examinations, we conduct experiment to compare four scheduling strategies, includingthe random, shortest distance, maximum free space, and decision-making models, and comparatively analyze the performance of the genetic algorithms, particle swarm optimization, simulated annealing, and differential evolution in optimizing the decision-making models. The experimental results demonstrates that the decision-making model based on the differential evolution algorithm performs the best, and the optimized decision-making model can find feasible solutions in the case scenarios and also obtain the optimal scheduling results.

The classic recommendation system focuses on the accuracy of recommendation. With the increase of users’ diversified needs, the diversity of recommendation results has attracted more and more attention. The accuracy and diversity of the recommendation always conflict with each other, and the traditional recommendation algorithms often ignore the difference of users’ activity in the system. A user stratification multi-objective recommendation algorithm is proposed based on the difference of users’ evaluation times on the items, which can provide better recommendation results for different users. Users are divided into three types with high, medium and low evaluation times, and three different initialization methods are proposed for the algorithm. By the comparative analysis of the initialization operations and parameter settings of the existing probability based multi-objective evolutionary algorithm, improved crossover and mutation operations are obtained. The experimental results verify that the enhanced multi-objective evolutionary algorithm can find better results with higher accuracy and diversity. Finally, a recommendation scheme based on user stratification is summarized, which helps to improve the recommendation effect for different users.

The existing expensive constrained multi-objective optimization algorithms based on surrogate models face two main issues. Firstly, the use of regression models to fit constraints introduces errors that affect the algorithm's search direction. Secondly, when the objective function is non-fittable, the performance of the regression model for fitting is poor. To address these issues, a collaborative expensive constrained multi-objective evolutionary optimization algorithm is proposed, which combines a classification model with a regression model. This method employs the classification model to roughly divide the search space, guiding the algorithm to quickly enter the feasible region and reducing the impact of constraint fitting errors. The regression model is then used to optimize the objective function within the feasible region. The collaboration of the two models allows the classification model to provide a general search direction while the regression model performs detailed modeling. This fusion of models not only considers the impact of constraint errors on the algorithm but also comprehensively addresses the fittability of the objective function, enabling a more comprehensive and accurate depiction of the characteristics of complex problems. As a result, it enhances the efficiency and effectiveness of the algorithm, providing an effective approach for further improving expensive constrained multi-objective optimization based on surrogate models.