Scholarlink

<p>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.</p> Graphical Abstract <p></p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

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

  • 期刊论文

  • 发表日期：

    2025年1月17日

查看全文
  • Zahra khoramipoor,
  • Saeed Farzin

摘要

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