A microgrid is an autonomous system that can realize self-control, protection, and management and is composed of distributed power sources, energy storage devices, loads, and control and protection devices. To achieve low operation costs, this paper a multi-strategy enhanced dwarf mongoose optimization algorithm (EDMO) for microgrid scheduling problem is proposed. In EDMO, the convergence speed is accelerated by introducing the golden Sine strategy, which generally makes it difficult to find new excellent solutions at a later stage, lead to a reduction in the population diversity and limiting the development capability, as well as introducing adaptive t-distribution variation to increase the population diversity and the introduction Lévy flight to enhance the algorithm's ability to jump out of the local optimum. The EDMO was compared with other nine algorithms applied to the microgrid optimal scheduling problem. The experimental results show that the proposed EDMO can achieved the lowest total cost, exhibits good performance and robustness, and is an effective method for solving the microgrid scheduling problem.
Published in | International Journal of Intelligent Information Systems (Volume 14, Issue 2) |
DOI | 10.11648/j.ijiis.20251402.12 |
Page(s) | 26-43 |
Creative Commons |
This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited. |
Copyright |
Copyright © The Author(s), 2025. Published by Science Publishing Group |
Microgrid Optimal, Dwarf Mongoose Optimization, Golden Sine Strategy, Adaptive t-distribution Mutation, Lévy Flight, Metaheuristic Optimization
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APA Style
Meng, W., Chen, S., Zhou, Y. (2025). Multi-strategy Enhanced Dwarf Mongoose Optimization Algorithm for Microgrid Optimal Scheduling Problem. International Journal of Intelligent Information Systems, 14(2), 26-43. https://doi.org/10.11648/j.ijiis.20251402.12
ACS Style
Meng, W.; Chen, S.; Zhou, Y. Multi-strategy Enhanced Dwarf Mongoose Optimization Algorithm for Microgrid Optimal Scheduling Problem. Int. J. Intell. Inf. Syst. 2025, 14(2), 26-43. doi: 10.11648/j.ijiis.20251402.12
@article{10.11648/j.ijiis.20251402.12, author = {Weiping Meng and Shijian Chen and Yongquan Zhou}, title = {Multi-strategy Enhanced Dwarf Mongoose Optimization Algorithm for Microgrid Optimal Scheduling Problem }, journal = {International Journal of Intelligent Information Systems}, volume = {14}, number = {2}, pages = {26-43}, doi = {10.11648/j.ijiis.20251402.12}, url = {https://doi.org/10.11648/j.ijiis.20251402.12}, eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ijiis.20251402.12}, abstract = {A microgrid is an autonomous system that can realize self-control, protection, and management and is composed of distributed power sources, energy storage devices, loads, and control and protection devices. To achieve low operation costs, this paper a multi-strategy enhanced dwarf mongoose optimization algorithm (EDMO) for microgrid scheduling problem is proposed. In EDMO, the convergence speed is accelerated by introducing the golden Sine strategy, which generally makes it difficult to find new excellent solutions at a later stage, lead to a reduction in the population diversity and limiting the development capability, as well as introducing adaptive t-distribution variation to increase the population diversity and the introduction Lévy flight to enhance the algorithm's ability to jump out of the local optimum. The EDMO was compared with other nine algorithms applied to the microgrid optimal scheduling problem. The experimental results show that the proposed EDMO can achieved the lowest total cost, exhibits good performance and robustness, and is an effective method for solving the microgrid scheduling problem. }, year = {2025} }
TY - JOUR T1 - Multi-strategy Enhanced Dwarf Mongoose Optimization Algorithm for Microgrid Optimal Scheduling Problem AU - Weiping Meng AU - Shijian Chen AU - Yongquan Zhou Y1 - 2025/06/03 PY - 2025 N1 - https://doi.org/10.11648/j.ijiis.20251402.12 DO - 10.11648/j.ijiis.20251402.12 T2 - International Journal of Intelligent Information Systems JF - International Journal of Intelligent Information Systems JO - International Journal of Intelligent Information Systems SP - 26 EP - 43 PB - Science Publishing Group SN - 2328-7683 UR - https://doi.org/10.11648/j.ijiis.20251402.12 AB - A microgrid is an autonomous system that can realize self-control, protection, and management and is composed of distributed power sources, energy storage devices, loads, and control and protection devices. To achieve low operation costs, this paper a multi-strategy enhanced dwarf mongoose optimization algorithm (EDMO) for microgrid scheduling problem is proposed. In EDMO, the convergence speed is accelerated by introducing the golden Sine strategy, which generally makes it difficult to find new excellent solutions at a later stage, lead to a reduction in the population diversity and limiting the development capability, as well as introducing adaptive t-distribution variation to increase the population diversity and the introduction Lévy flight to enhance the algorithm's ability to jump out of the local optimum. The EDMO was compared with other nine algorithms applied to the microgrid optimal scheduling problem. The experimental results show that the proposed EDMO can achieved the lowest total cost, exhibits good performance and robustness, and is an effective method for solving the microgrid scheduling problem. VL - 14 IS - 2 ER -