## 6 Summary and Conclusions  
This paper presents a bi-objective mathematical model to address the energy-production-scheduling problem in a biomass-integrated renewable energy microgrid with energy storage. The LSTM-PMOEVO framework is used to solve the model, with the goal of optimizing total cost and CO\({}_{2}\) emissions. Five optimization strategies are designed to improve the performance of PMOEVO, tailored to the specific characteristics of the problem: robust optimization considering forecast errors, adaptive alpha and gamma decay, adaptive random movement, knowledge-driven neighborhood structure, and Pareto selection with Elimination Of Homogeneous Individuals.  
Repeated experiments on the dataset show that the LSTM-PMOEVO framework performs exceptionally well. The optimization strategies improve the algorithm's robustness and effectively balance exploration and exploitation during the search process. Comparisons with other high-performance algorithms show that the PMOEVO obtains most of the Pareto front solutions while maintaining solution diversity.  
Although the research framework designed in this paper has achieved good performance on the studied dataset, there are still some limitations. For example, the PMOEVO algorithm designed in this paper has a strong dependency on parameters, requiring repeated experiments to determine the optimal parameters. Additionally, the dataset used in this paper does not fully represent the energy generation and usage characteristics of all types of communities, and future research should further explore the applicability of the algorithm in other regions. Furthermore, this paper simplifies certain energy conversion processes, which may limit its scientific accuracy and practical application.  
Future work will focus on the following directions:  
(1) Developing optimization models for more complex microgrid systems to enhance the practical applicability of the model, including the sale of surplus energy to the grid and the price variations in purchasing electricity from the grid at different times.  
(2) By systematically collecting and structuring relevant knowledge combined with additional knowledge-driven optimization strategies, more efficient optimization algorithms can be designed to obtain better solutions within limited computational time.  
(3) Additionally, machine learning models will be considered for integration into the optimization process, such as learning from historical energy-scheduling data and results, using machine learning to generate a better initial solution set, and further optimizing based on metaheuristic algorithms.  
Conceptualization, Z.W. and Y.Z.; formal analysis, Z.W.; funding acquisition, Y.Z.; investigation, Z.W.; methodology, Z.W.; project administration, Y.Z.; software, Z.W.; supervision, Y.Z.; validation, Z.W. and Y.Z.; visualization, Z.W.; writing--original draft, Z.W.; writing--review and editing, Y.Z. All authors have read and agreed to the published version of the manuscript.  
This research received no funding.  
The original contributions presented in this study are included in the article. Further inquiries can be directed to the corresponding author.  
The authors declare no conflicts of interest.