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An Augmented Artificial Bee Colony Algorithm with Adaptive Neighborhood Search for Solving Systems of Differential Equations | ||
| Computational Methods for Differential Equations | ||
| مقالات آماده انتشار، پذیرفته شده، انتشار آنلاین از تاریخ 04 مرداد 1405 اصل مقاله (1.69 M) | ||
| نوع مقاله: Research Paper | ||
| شناسه دیجیتال (DOI): 10.22034/cmde.2026.71492.3593 | ||
| نویسنده | ||
| Aliakbar Tajari Siahmarzkooh* | ||
| Department of Computer Sciences, Golestan University, Gorgan, Iran. | ||
| چکیده | ||
| The numerical solution of systems of ordinary differential equations (ODEs) remains computationally challenging, particularly for stiff or high-dimensional problems. While metaheuristic algorithms like the Artificial Bee Colony (ABC) offer global search capabilities, they often exhibit slow convergence and inefficiency in the exploitation phase when applied to precise ODE solving. This paper proposes an Augmented ABC (A-ABC) algorithm to address these shortcomings. The core innovation lies in integrating an Adaptive Neighborhood Search (ANS) mechanism into the employed bee phase. ANS dynamically adjusts the perturbation step size based on the fitness landscape, enabling a more refined local search around promising solutions. Applied to three benchmark systems of ODEs (including a stiff problem), the proposed A-ABC algorithm achieves a 6.45% improvement in mean MSE (averaged over three benchmarks) and a 6.15% reduction in the number of function evaluations compared to the standard ABC, based on 30 independent runs. Compared to the Gbest-guided ABC and a hybrid PSO-ABC, A-ABC shows improvements of 1.5–3.4% in mean MSE. A sensitivity analysis confirms that the ANS mechanism is robust to the choice of its parameters (ψmin, ψmax), with performance variation below 2%. These results confirm that the Adaptive Neighborhood Search mechanism enhances exploitation without compromising global search capability. | ||
| کلیدواژهها | ||
| Artificial Bee Colony Algorithm؛ Adaptive Neighborhood Search؛ Systems of Ordinary Differential Equations؛ Metaheuristic Optimization؛ Numerical Solution | ||
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