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<p>The dynamic economic emissions dispatch (DEED) problem in power systems aims to minimize fuel costs and emissions while meeting load demands and operational constraints. The DEED problem’s high dimensionality and multi-objective nature pose challenges for traditional optimization methods. In contrast to traditional optimization methods, robust optimization algorithms, which are less sensitive to initial conditions, offer better solutions. Therefore, this paper proposes an augmented multi-objective multi-verse optimizer (AMOMVO) algorithm. In the AMOMVO algorithm, we employ a new augmented strategy to adjust the wormhole existence probability parameter, enhancing the algorithm’s exploration capability. Additionally, an augmented strategy for the travelling distance rate balances global exploration in early iterations with local exploitation later, avoiding local optima. Finally, we design a differential mutation search mechanism that significantly improves global search performance. AMOMVO excels in DEED problems with 5, 10, and 15 units.</p>

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An augmented multi-objective multi-verse optimizer algorithm for solving dynamic economic emission dispatch problems

  • 期刊论文

  • 发表日期：

    2025年6月19日

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  • Huixian Tian,
  • Yingjie Song

摘要

The dynamic economic emissions dispatch (DEED) problem in power systems aims to minimize fuel costs and emissions while meeting load demands and operational constraints. The DEED problem’s high dimensionality and multi-objective nature pose challenges for traditional optimization methods. In contrast to traditional optimization methods, robust optimization algorithms, which are less sensitive to initial conditions, offer better solutions. Therefore, this paper proposes an augmented multi-objective multi-verse optimizer (AMOMVO) algorithm. In the AMOMVO algorithm, we employ a new augmented strategy to adjust the wormhole existence probability parameter, enhancing the algorithm’s exploration capability. Additionally, an augmented strategy for the travelling distance rate balances global exploration in early iterations with local exploitation later, avoiding local optima. Finally, we design a differential mutation search mechanism that significantly improves global search performance. AMOMVO excels in DEED problems with 5, 10, and 15 units.