Authors:
Awaz Ahmed Shaban, Saman M. Almufti, Ridwan B. Marqas

Addresses:
Department of Information Technology, Akre Technical College of Informatics, Akre University for Applied Sciences, Akre, Duhok, Iraq. Department of Computer Science, College of Science, Knowledge University, Erbil, Kurdistan Region, Iraq.

Abstract:

Economic dispatch (ED) is a critical optimization problem in power systems, aiming to schedule generator outputs to meet load demand at minimal cost. Traditional formulations often model ED as a quadratic equation; however, the problem is inherently nonconvex due to factors such as ramp-rate limits, valve-point loading, and prohibited operating zones. These complexities, coupled with the increasing scale of power grids, pose significant challenges for traditional optimization techniques. This paper introduces a modified bat algorithm (MBA) designed to enhance both exploration and exploitation capabilities for solving the ED problem. The proposed MBA incorporates adaptive parameter control and an elitist learning strategy inspired by Adaptive Particle Swarm Optimization (APSO) to improve robustness and convergence. The performance of the MBA is evaluated on benchmark test systems, and the results are compared against those obtained using the original BA, genetic algorithm (GA), and particle swarm optimization (PSO). The results demonstrate improvements in convergence speed, solution quality, and robustness, making it a promising candidate for advanced economic dispatch optimization.

Keywords: Economic Dispatch; Modified Bat Algorithm; Metaheuristic Optimization; Adaptive Parameter Control; Swarm Intelligence; Adaptive Particle Swarm Optimization; Constraint Handling; Genetic Algorithm.

Received on: 02/10/2024, Revised on: 23/12/2024, Accepted on: 13/02/2025, Published on: 07/06/2025

DOI: 10.69888/FTSTPL.2025.000437

FMDB Transactions on Sustainable Technoprise Letters, 2025 Vol. 3 No. 2, Pages: 59-72

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