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This paper introduces an improved differential evolution algorithm based on cooperative multi-population (CMp-DE for short), which combines diverse population collaboration mechanisms and catalytic factors into an improved differential evolution framework. By harnessing various population collaboration mechanisms, the algorithm enhances the diversity of individuals within populations during initial iterations and reduces it during later iterations, thereby harmonizing the algorithm's exploratory and exploitative capabilities. Furthermore, a novel mutation operator is proposed that divides the iterative process into exploration and exploitation phases, thereby augmenting the algorithm's global exploration prowess. Lastly, a catalytic operator is introduced to generate new individuals near post-crossover individuals based on a specified rule, which enhances the algorithm's ability to escape local optima and increasing stability. The proposed CMp-DE is benchmarked against the CEC2017 benchmark test functions and compared against 13 algorithms, including five differential evolution algorithms and their variants, as well as eight state-of-the-art metaheuristic optimization algorithms. This evaluation assesses the CMp-DE's solution accuracy, convergence, stability, and scalability. Finally, the applicability of CMp-DE is validated by addressing six practical optimization problems. The experimental results show that CMp-DE surpasses other algorithms in terms of both convergence accuracy and robustness. Moreover, integrating a catalytic operator with other optimization algorithms notably boosts performance in convergence accuracy and stability. The inclusion of the catalytic operator has significantly enhanced the performance of algorithms compared to their performance before its addition. This underscores the potential of the catalytic operator in improving the performance of various algorithms, particularly in terms of convergence accuracy and robustness. [ABSTRACT FROM AUTHOR]

This paper introduces an improved differential evolution algorithm based on cooperative multi-population (CMp-DE for short), which combines diverse population collaboration mechanisms and catalytic factors into an improved differential evolution framework. By harnessing various population collaboration mechanisms, the algorithm enhances the diversity of individuals within populations during initial iterations and reduces it during later iterations, thereby harmonizing the algorithm's exploratory and exploitative capabilities. Furthermore, a novel mutation operator is proposed that divides the iterative process into exploration and exploitation phases, thereby augmenting the algorithm's global exploration prowess. Lastly, a catalytic operator is introduced to generate new individuals near post-crossover individuals based on a specified rule, which enhances the algorithm's ability to escape local optima and increasing stability. The proposed CMp-DE is benchmarked against the CEC2017 benchmark test functions and compared against 13 algorithms, including five differential evolution algorithms and their variants, as well as eight state-of-the-art metaheuristic optimization algorithms. This evaluation assesses the CMp-DE's solution accuracy, convergence, stability, and scalability. Finally, the applicability of CMp-DE is validated by addressing six practical optimization problems. The experimental results show that CMp-DE surpasses other algorithms in terms of both convergence accuracy and robustness. Moreover, integrating a catalytic operator with other optimization algorithms notably boosts performance in convergence accuracy and stability. The inclusion of the catalytic operator has significantly enhanced the performance of algorithms compared to their performance before its addition. This underscores the potential of the catalytic operator in improving the performance of various algorithms, particularly in terms of convergence accuracy and robustness. [ABSTRACT FROM AUTHOR]

Because of the COVID-19 pandemic, Chinese hospitals are increasing their efforts to control the number of patients to decrease cross-infection risks. In this paper, we investigate the flexible physician scheduling problem for emergency departments (EDs), considering the constraint of the maximum number of emergency patients in one time period. We model the ED service system as a time-varying queue with returns and formulate the physician scheduling problem as a mixed-integer programming model. To solve the scheduling problem effectively, we design a learning-based two-stage optimization algorithm. In the first stage, we solve the physician staffing problem, in which two effective acceleration strategies based on machine learning models are developed. In the second stage, we propose a branch-and-price algorithm to determine the physician scheduling plan. Numerical experiments based on real-life data show that the proposed two-stage algorithm can effectively solve our flexible physician scheduling problem, and the scheduling plan obtained by the proposed two-stage algorithm can significantly improve the physician schedule without involving more physicians. [ABSTRACT FROM AUTHOR]

The collaborative optimization dispatching of multiple energy flows plays a crucial role in achieving the economic and efficient low-carbon operation of integrated energy systems (IESs). However, the dispatching problem for IESs is characterized by high dimensionality, non-linearity, and complex coupling. Furthermore, the integration of renewable energy sources and flexible loads has transformed the IES into a complex dynamic system with significant uncertainty. Traditional intelligent optimization algorithms exhibit poor adaptability and lengthy solution computation time when tackling such problems. In contrast, deep reinforcement learning (DRL), as an interactive trial-and-error learning method, has shown improved decision-making capabilities. In view of this, a data-driven soft actor-critic (SAC) deep reinforcement learning-based approach is proposed in this paper for interval optimal dispatch of IESs considering multiple uncertainties. First, the basic principle of SAC reinforcement learning is introduced in detail, and the basic framework of reinforcement learning for interval optimal scheduling of IESs is constructed. Then, the environment model of agent interaction is constructed, and the action and state space of DRL, as well as the reward mechanism and neural network structure, are designed. Finally, a typical IES case is experimentally analyzed and compared with three popular DRL algorithms and five state-of-the-art intelligent optimization algorithms. The experimental results demonstrate the advantages and effectiveness of the proposed method in solving the optimal dispatching of IESs. [ABSTRACT FROM AUTHOR]