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<p>This paper introduces the use of modern metaheuristic optimization techniques: Gorilla Troops Optimizer (GTO), inspired by the social behavior of gorilla troops in the wild, and Artificial rabbits optimization (ARO), inspired by the survival strategies of rabbits in nature, including detour foraging and random hiding.&#xa0;These are combined to form a hybrid algorithm called hybrid Artificial rabbits Gorilla Troops Optimizer (ARGTO). The proposed ARGTO algorithm aims to reach exploration-exploitation balance to improve search efficiency. The presented algorithms were tested on seven mathematical optimization problems, and in order to make a more accurate comparison, the average optimization results and corresponding standard deviation results are calculated by running these algorithms 20 times for each optimization problem. A comparative analysis of the presented algorithms was conducted, showing that ARGTO achieved better performance. Initially, ARGTO was evaluated against its constituent algorithms, Artificial Rabbit Optimization (ARO) and Gorilla Troop Optimization (GTO). Subsequently, its efficacy was benchmarked against alternative optimization techniques, including Northern Goshawk Optimization (NGO), manta ray foraging optimization (MRFO), Dung Beetle Optimizer (DBO), and Runge Kutta optimizer (RUN). The principal aim of this research is to resolve power Economic Load Dispatch (ELD) problems, incorporating a comprehensive set of operational constraints, such as valve-point effects, ramp rate limits, prohibited operating zones, and multiple generator fuel options. Notably, the presented algorithm incorporates a self-adaptation mechanism, effectively mitigating the complexities associated with parameter tuning for diverse optimization problem characteristics. The efficacy of the ARGTO approach in addressing ELD with substantial nonlinearities is substantiated through experiments conducted on five well-established test power systems that comprise 6, 10, 11, 15, and 110 generation units. These results are compared with the outcomes produced by various other optimization methods proposed in recent literature. The performance of the ARGTO technique is demonstrated by its ability to minimize total costs, achieve rapid convergence, and maintain solution consistency.</p>

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ARGTO-ELD: efficient economic load dispatch solution in power systems using hybrid artificial rabbits and Gorilla Troop Optimization algorithm

  • 期刊论文

  • 发表日期：

    2026年5月5日

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  • Mohamed H. Hassan,
  • Salah Kamel,
  • Mahmoud A. El-Dabah

摘要

This paper introduces the use of modern metaheuristic optimization techniques: Gorilla Troops Optimizer (GTO), inspired by the social behavior of gorilla troops in the wild, and Artificial rabbits optimization (ARO), inspired by the survival strategies of rabbits in nature, including detour foraging and random hiding. These are combined to form a hybrid algorithm called hybrid Artificial rabbits Gorilla Troops Optimizer (ARGTO). The proposed ARGTO algorithm aims to reach exploration-exploitation balance to improve search efficiency. The presented algorithms were tested on seven mathematical optimization problems, and in order to make a more accurate comparison, the average optimization results and corresponding standard deviation results are calculated by running these algorithms 20 times for each optimization problem. A comparative analysis of the presented algorithms was conducted, showing that ARGTO achieved better performance. Initially, ARGTO was evaluated against its constituent algorithms, Artificial Rabbit Optimization (ARO) and Gorilla Troop Optimization (GTO). Subsequently, its efficacy was benchmarked against alternative optimization techniques, including Northern Goshawk Optimization (NGO), manta ray foraging optimization (MRFO), Dung Beetle Optimizer (DBO), and Runge Kutta optimizer (RUN). The principal aim of this research is to resolve power Economic Load Dispatch (ELD) problems, incorporating a comprehensive set of operational constraints, such as valve-point effects, ramp rate limits, prohibited operating zones, and multiple generator fuel options. Notably, the presented algorithm incorporates a self-adaptation mechanism, effectively mitigating the complexities associated with parameter tuning for diverse optimization problem characteristics. The efficacy of the ARGTO approach in addressing ELD with substantial nonlinearities is substantiated through experiments conducted on five well-established test power systems that comprise 6, 10, 11, 15, and 110 generation units. These results are compared with the outcomes produced by various other optimization methods proposed in recent literature. The performance of the ARGTO technique is demonstrated by its ability to minimize total costs, achieve rapid convergence, and maintain solution consistency.