Multi-strategy Enhanced Dwarf Mongoose Optimization Algorithm for Microgrid Optimal Scheduling Problem

Weiping Meng; Shijian Chen; Yongquan Zhou

doi:doi:10.11648/j.ijiis.20251402.12

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Multi-strategy Enhanced Dwarf Mongoose Optimization Algorithm for Microgrid Optimal Scheduling Problem

Weiping Meng

, Shijian Chen

, Yongquan Zhou^*

Published in International Journal of Intelligent Information Systems (Volume 14, Issue 2)

Received: 20 April 2025 Accepted: 3 May 2025 Published: 3 June 2025

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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.

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

Keywords

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

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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

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AMA 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

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@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}
}

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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  -

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Author Information

Weiping Meng

College of Artificial Intelligence, Guangxi Minzu University, Nanning, China

http://orcid.org/0009-0000-2693-7457
Shijian Chen

College of Artificial Intelligence, Guangxi Minzu University, Nanning, China

http://orcid.org/0009-0004-5177-760X
Yongquan Zhou

College of Artificial Intelligence, Guangxi Minzu University, Nanning, China Guangxi Key Laboratories of Hybrid Computation and IC Design Analysis, Nanning, China

Contact Email

http://orcid.org/0000-0003-4404-952X

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Plain Text BibTeX RIS

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

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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

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AMA 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

Copy | Download

@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}
}

Copy | Download

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  -

Copy | Download