Improved Snow Geese Algorithm with a Probability-Based Convergence Factor for Solving Economic Load Dispatch Problems.

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Bibliographic Details
Title:	Improved Snow Geese Algorithm with a Probability-Based Convergence Factor for Solving Economic Load Dispatch Problems.
Authors:	Xiong, Zhitong¹ 3316662457@qq.com
Source:	IAENG International Journal of Computer Science. Jun2026, Vol. 53 Issue 6, p2305-2315. 11p.
Subjects:	Load dispatching in electric power systems, Swarm intelligence, Nonconvex programming, Optimization algorithms, Metaheuristic algorithms
Abstract:	To enhance the convergence efficiency and solution accuracy of swarm intelligence algorithms for complex nonconvex optimization problems, this paper proposes an improved Snow Geese Algorithm with a Probability Based Convergence Factor, termed CF-ISGA. The introduced convergence factor enables adaptive step size adjustment, effectively balancing global exploration and local exploitation. The effectiveness of CF-ISGA is validated on the CEC2022 benchmark functions and a 40 unit Economic Load Dispatch (ELD) problem, with comparisons against several representative metaheuristic algorithms. Experimental results demonstrate that CF-ISGA achieves faster convergence, higher solution accuracy, and better robustness. In particular, CF-ISGA obtains the minimum generation cost in the 40 unit ELD system, confirming its strong global optimization capability. [ABSTRACT FROM AUTHOR]
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Database:	Engineering Source

Description
Abstract:	To enhance the convergence efficiency and solution accuracy of swarm intelligence algorithms for complex nonconvex optimization problems, this paper proposes an improved Snow Geese Algorithm with a Probability Based Convergence Factor, termed CF-ISGA. The introduced convergence factor enables adaptive step size adjustment, effectively balancing global exploration and local exploitation. The effectiveness of CF-ISGA is validated on the CEC2022 benchmark functions and a 40 unit Economic Load Dispatch (ELD) problem, with comparisons against several representative metaheuristic algorithms. Experimental results demonstrate that CF-ISGA achieves faster convergence, higher solution accuracy, and better robustness. In particular, CF-ISGA obtains the minimum generation cost in the 40 unit ELD system, confirming its strong global optimization capability. [ABSTRACT FROM AUTHOR]
ISSN:	1819656X