## 1 Introduction
introduced an innovative optimization technique called the Energy Valley Optimizer (EVO) [13]. [PERSON] et al. developed a Fuzzy-Logic-based energy management system for grid-connected microgrids with renewable energy and storage, optimizing it offline using NSGA-II [14]. Compared to other evolutionary algorithms, NSGA-II can find a more diverse set of solutions in terms of Pareto optimality [15]. However, as the design configuration of microgrids becomes more complex, and the diversification of production costs due to the addition of multiple devices and carbon taxes occurs, the solution space of the optimization problem becomes significantly larger and more complex, making it challenging for traditional algorithms to effectively balance exploration and exploitation of the population. Moreover, due to the changes in electricity demand, heat demand, and renewable resources, there is great uncertainty in the microgrids operation. Effective short-term load and renewable energy resource forecasting is essential for the optimization and economic operation of microgrids. Research on electricity demand forecasting using LSTM has become established. [PERSON] et al. introduced a model for predicting short-term load demands in microgrids, utilizing Complete Ensemble Empirical Mode Decomposition with Adaptive Noise alongside LSTM, which avoids the influence of seasonal, periodic, and random fluctuations in the original load data of microgrids on the accuracy of load forecasting [16]. [PERSON] et al. developed an LSTM-based model for predicting the net load of microgrids with solar and wind power, enhancing energy management capabilities [17]. Furthermore, [PERSON] et al. designed a method that integrated Long LSTM with Particle Swarm Optimization (PSO), leveraging LSTM's ability to predict the active movement tendencies of particles for more efficient and faster optimization [18]. [PERSON] et al. proposed BE-LSTM, which combined BE for feature selection with LSTM for time series forecasting [19]. Multiple studies have conducted relevant research [20; 21; 22]. However, few authors consider the impact of prediction model errors in the iterative process of microgrid configuration and scheduling optimization models. In conclusion, research on microgrid-scheduling optimization faces challenges such as unpredictable fluctuations and the complexity of solving these systems. The capacity scheduling has not been combined with forecasts of future electricity and heat demand in previous studies. A comprehensive scheduling optimization model has been developed, encompassing wind turbines, solar panels, biomass gasifiers, batteries, the electricity grid, and the heat grid. Additionally, a Parallel Multi-Objective Energy Valley Optimizer (PMOEVO) has been designed. The integrated dispatching optimization model for the microgrid is solved using the LSTM-PMOEVO framework. Compared to previous studies, the contributions of this paper are as follows:  
(1) A comprehensive scheduling-optimization model has been developed, considering wind turbines, solar panels, biomass gasifiers, batteries, electricity grids, and heat grids simultaneously. (2) For the microgrid-scheduling problem considered in this paper, a prediction model for electrical and thermal loads was built based on LSTM. (3) A high-performance PMOEVO has been designed. The PMOEVO incorporates a robust optimization strategy to account for prediction errors, addressing the unpredictability of random fluctuations and improving solution robustness. To address the complexity of solving such systems, PMOEVO incorporates a series of optimization strategies to improve search efficiency within the solution space. These strategies include adaptive alpha and gamma decay, adaptive random movement, a knowledge-driven neighborhood structure, and Pareto Selection with Elimination of Homogeneous Individuals. The structure of this study is organized as follows: Section 2 presents the problem formulation and mathematical model. Section 3 introduces the LSTM-based electricity and heat load prediction model.