A Methodology to Improving the Performance of MOAHA Optimization Algorithm using Chaos Theory; Principle and Application in Optimal Reservoir Operation A Methodology to Improving the Performance of MOAHA Optimization Algorithm using Chaos Theory; Principle and Application in Optimal Reservoir Operation

• Published in: Water resources management Vol. 39; no. 6; pp. 2819 - 2840
• Main Authors: khoramipoor, Zahra, Farzin, Saeed
• Format: Journal Article
• Language: English
• Published: Dordrecht Springer Netherlands 01.04.2025; Springer Nature B.V
• Subjects: Algorithms; Atmospheric Sciences; Chaos theory; Civil Engineering; computer software; Earth and Environmental Science; Earth Sciences; energy; Environment; Evaporation; Geotechnical Engineering & Applied Earth Sciences; hummingbirds; Hydroelectric power; Hydrogeology; Hydrology/Water Resources; Multiple objective analysis; Optimization; Performance enhancement; Reservoir management; Reservoir operation; Theories; water; water power; Hydropower energy; Reservoir operation; MOAHA algorithm; Chaos theory; Cascade multi-purpose reservoirs
• ISSN: 0920-4741; 1573-1650
• DOI: 10.1007/s11269-025-04092-y

This study introduces a novel algorithm, CMOAHA, which integrates the multi-objective hummingbird optimizer with chaos theory to optimize multi-reservoir system management. The algorithm aims to maximize hydropower energy production and minimize evaporation losses. The performance of CMOAHA is compared to that of MOGWO, MOALO, and NSGA-II algorithms. Using evaluation criteria such as MS, CV, and MID, CMOAHA demonstrates superior efficiency, achieving values of CV = 4,834,269.468, MS = 19,359,123.542, and MID = 6,895,142.911. In contrast, the gray wolf optimizer (MOGWO) shows the lowest performance, with CV = 17,602,966.401 and MID = 17,429,422.893. Rankings confirm that the improved hummingbird algorithm achieves the highest efficiency, with a rating of 0.95, while NSGA-II ranks lowest. Moreover, the output of the CMOAHA algorithm closely aligns with results from LINGO software, achieving a 96.73% match to the global optimum. These findings highlight the enhanced performance and strong capabilities of the hummingbird algorithm when augmented with chaos theory. The results showed that the multi-objective artificial hummingbird algorithm enhanced by chaos theory provides better accuracy and certainty compared to other algorithms. Graphical Abstract