A Multi-Strategy Improved Moth-Flame Algorithm and Its Application to Power System Stabilizer Tuning

Lyuyang Ye

doi:10.47297/taposatWSP2633-456919.20260703

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A Multi-Strategy Improved Moth-Flame Algorithm and Its Application to Power System Stabilizer Tuning

7卷第3期

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A Multi-Strategy Improved Moth-Flame Algorithm and Its Application to Power System Stabilizer Tuning
A Multi-Strategy Improved Moth-Flame Algorithm and Its Application to Power System Stabilizer Tuning
Guangzhou College of Commerce, Guangzhou, Guangdong, 511363, China
2026年7卷第3期页码：94-99
纸质出版：2026-03-30
DOI：10.47297/taposatWSP2633-456919.20260703
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Lyuyang Ye. (2026). A Multi-Strategy Improved Moth-Flame Algorithm and Its Application to Power System Stabilizer Tuning. 科技理论与实践（英文版）, 7(3), 94-99.

Ye Lyuyang.A Multi-Strategy Improved Moth-Flame Algorithm and Its Application to Power System Stabilizer Tuning[J].Theory and Practice of Science and Technology,2026,07(03):94-99.
Lyuyang Ye. (2026). A Multi-Strategy Improved Moth-Flame Algorithm and Its Application to Power System Stabilizer Tuning. 科技理论与实践（英文版）, 7(3), 94-99. DOI： 10.47297/taposatWSP2633-456919.20260703.

Ye Lyuyang.A Multi-Strategy Improved Moth-Flame Algorithm and Its Application to Power System Stabilizer Tuning[J].Theory and Practice of Science and Technology,2026,07(03):94-99. DOI： 10.47297/taposatWSP2633-456919.20260703.

摘要

Abstract

To address the issues that the Moth-Flame Optimization (MFO) algorithm tends to fall into local optima and suffers from insufficient convergence accuracy

a multi-strategy improved Moth-Flame Optimization algorithm (MMFO) is proposed. First

the Sine-Tent-Cosine chaotic map is employed to initialize the population

enhancing population diversity. Second

the oscillation mechanism of the Sine Cosine Algorithm (SCA) is integrated to balance global exploration and local exploitation. Finally

the somersault foraging strategy is introduced to strengthen the algorithm's ability to escape local optima. The proposed MMFO is applied to optimize the parameters of two types of Power System Stabilizers (PSS)

namely Lead-Lag and PID controllers. Simulations are conducted on a Single-Machine Infinite-Bus (SMIB) system under four error criteria: IAE

ISE

ITAE

and ITSE. Comparative analyses with MFO

Particle Swarm Optimization (PSO)

Grey Wolf Optimizer (GWO)

Flower Pollination Algorithm (FPA)

and Jaya algorithm demonstrate that the PSS optimized by MMFO achieves the best comprehensive performance in suppressing low-frequency oscillations

reducing overshoot

and shortening settling time

thereby verifying its effectiveness and superiority.

关键词

Keywords

references

Demello F P, Concordia C. Concepts of synchronous machine stability as affected by excitation control[J]. IEEE Transactions on Power Apparatus and Systems, 1969, 88(4): 316-329.

Kundur P, Balu N J, Lauby M G. Power system stability and control[M]. New York: McGraw-Hill, 1994.

Kennedy J, Eberhart R. Particle swarm optimization[C]//Proceedings of ICNN'95 - International Conference on Neural Networks. IEEE, 1995, 4: 1942-1948.

Jolfaei M G, Sharaf A M, Shariatmadar S M, et al. A hybrid PSS–SSSC GA-stabilization scheme for damping power system small signal oscillations[J]. International Journal of Electrical Power & Energy Systems, 2016, 75: 337-344.

Mirjalili S. Moth-flame optimization algorithm: A novel nature-inspired heuristic paradigm[J]. Knowledge-Based Systems, 2015, 89: 228-249.

Hua Z, Zhou Y, Huang H. Cosine-transform-based chaotic system for image encryption[J]. Information Sciences, 2019, 480: 403-419.

Mirjalili S. SCA: A sine cosine algorithm for solving optimization problems[J]. Knowledge-Based Systems, 2016, 96: 120-133.

Zhao W, Zhang Z, Wang L. Manta ray foraging optimization: An effective bio-inspired optimizer for engineering applications[J]. Engineering Applications of Artificial Intelligence, 2020, 87: 103300.

Anderson P M, Fouad A A. Power system control and stability[M]. Piscataway, NJ: IEEE Press, 2003.

Patel P, Patel J, Sidhpuria H. Study of excitation system and power system stabilizer for single machine infinite bus system[J]. International Journal of Engineering Research & Technology (IJERT), 2021, 10(6).

Shayeghi H, Shayanfar H A, Asefi S, et al. Optimal tuning and comparison of different power system stabilizers using different performance indices via Jaya algorithm[C]//Proceedings of the International Conference on Scientific Computing (CSC). The Steering Committee of The World Congress in Computer Science, Computer Engineering and Applied Computing (WorldComp), 2016: 34.