Due to the increasing consumer demand for custom products, aluminum alloy component creep forming manufacturing has shifted towards production modes designed for multiple varieties and small batches, leading to problems such as complex production organization and low production efficiency. In the specific case of modern large-scale aluminum alloy aerospace components, the manufacturing requirements cannot be satisfied. According to the production characteristics and process requirements in this industry, a many-objective, whole-process production scheduling model was established, and a residual rectangle-based many-objective evolutionary algorithm (RTEA) was developed to solve it effectively. The RTEA uses the residual rectangle method in the decoding phase for autoclave filling, which improves the productivity of the autoclave. We further designed a three-stage environmental selection strategy to strengthen the balance of convergence and diversity and increase the selection pressure in the evolutionary process. Computational experiments were performed using industrial datasets relative to aerospace components and engineering production data. The advantages and competitiveness of the comprehensive production scheduling model and the RTEA were verified, as evidenced by an increase in production line efficiency of 20%. In conclusion, the proposed approach offers an effective solution to the many-objective production scheduling problem hindering aluminum alloy creep forming component production.

Axial blood pumps serve as vital auxiliary therapeutic devices for patients with end-stage heart failure, and parameter optimization of the controller is critical to improve system performance. Existing optimization methods cannot satisfy the parameter optimization requirements of variable-speed axial blood pump controllers in terms of optimization accuracy. Therefore, this paper investigates a many-objective optimization method adapted to the operating characteristics of blood pumps. Firstly, a many-objective optimization model is established for the controller. To efficiently solve the proposed model, an optimization algorithm integrating deep reinforcement learning, named DQN-NSGA-CT, is developed. On the basis of the population evolution state, the optimal strategy learned by DQN dynamically adjusts the crossover probability and mutation probability, which adaptively balances population diversity in the early iteration stage and convergence speed in the later iteration stage. Meanwhile, a novel environmental selection strategy is used to reconcile population convergence and diversity. To select the best compromise solution, an entropy weight–Copula–TOPSIS comprehensive evaluation method is proposed, which realizes objective weight assignment, objective correlation correction and multi-attribute ranking. Experimental results verify the efficiency of the DQN-NSGA-CT algorithm in solving the many-objective optimization model of the controller. The research addresses the many-objective optimization problem of variable-speed axial blood pump control.