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<p>While the Manta Ray Foraging Optimization (MRFO) methodology has shown promising results in tackling complex optimization problems, it has certain limitations, including restricted exploitation potential and a decline in population diversity, which can hinder its effectiveness in some applications. To address these shortcomings, this research introduces the Enhanced Manta Ray Foraging Optimization (EMRFO) algorithm, which incorporates high- and low-velocity ratios&#xa0;to improve the balance between exploitation and exploration. The EMRFO algorithm undergoes thorough testing against seven benchmark functions and is compared with several established optimization methods, including MRFO, marine predators algorithm (MPA), artificial rabbits optimization (ARO), grey wolf optimizer (GWO), dung beetle optimizer (DBO), and pelican optimization algorithm (POA). Simulation results indicate that EMRFO consistently outperforms its competitors, achieving superior solutions and faster convergence rates across most benchmark functions. Furthermore, the algorithm exhibits significant resilience and adaptability to different optimization parameters, making it suitable for a wide range of real-world applications, especially in the context of microgrid systems. These findings demonstrate that EMRFO is a highly effective optimization method, offering substantial improvements over other algorithms. In addition to algorithmic advancements, this study presents an isolated hybrid power system designed to ensure reliable and efficient energy supply. The system includes a photovoltaic (PV) unit, a biomass system, and a hydro-pumped storage unit (HPSU), with optimal sizing achieved through various optimization techniques, including EMRFO and other well-established algorithms such as MRFO, zebra optimization algorithm (ZOA), whale optimization algorithm (WOA), and hunger games search (HGS). Although all algorithms performed well, EMRFO consistently delivered the best results, achieving the lowest objective function and optimal sizing for the hybrid power system.</p>

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Optimal sizing of hybrid PV/biomass/hydro-pumped storage unit systems using an enhanced manta ray foraging optimizer: a benchmark and comparative study

  • 期刊论文

  • 发表日期：

    2025年5月7日

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  • Hoda Abd El-Sattar ,
  • Salah Kamel,
  • Moahmed H. Hassan,
  • Francisco Jurado

摘要

While the Manta Ray Foraging Optimization (MRFO) methodology has shown promising results in tackling complex optimization problems, it has certain limitations, including restricted exploitation potential and a decline in population diversity, which can hinder its effectiveness in some applications. To address these shortcomings, this research introduces the Enhanced Manta Ray Foraging Optimization (EMRFO) algorithm, which incorporates high- and low-velocity ratios to improve the balance between exploitation and exploration. The EMRFO algorithm undergoes thorough testing against seven benchmark functions and is compared with several established optimization methods, including MRFO, marine predators algorithm (MPA), artificial rabbits optimization (ARO), grey wolf optimizer (GWO), dung beetle optimizer (DBO), and pelican optimization algorithm (POA). Simulation results indicate that EMRFO consistently outperforms its competitors, achieving superior solutions and faster convergence rates across most benchmark functions. Furthermore, the algorithm exhibits significant resilience and adaptability to different optimization parameters, making it suitable for a wide range of real-world applications, especially in the context of microgrid systems. These findings demonstrate that EMRFO is a highly effective optimization method, offering substantial improvements over other algorithms. In addition to algorithmic advancements, this study presents an isolated hybrid power system designed to ensure reliable and efficient energy supply. The system includes a photovoltaic (PV) unit, a biomass system, and a hydro-pumped storage unit (HPSU), with optimal sizing achieved through various optimization techniques, including EMRFO and other well-established algorithms such as MRFO, zebra optimization algorithm (ZOA), whale optimization algorithm (WOA), and hunger games search (HGS). Although all algorithms performed well, EMRFO consistently delivered the best results, achieving the lowest objective function and optimal sizing for the hybrid power system.