Application of Artificial Rabbits Optimization Algorithm to the Economic Load Dispatch Problem

Samal, Padarbinda; Mohapatra, Alivarani; Samal, Sarita

doi:10.1109/incet57972.2023.10170612

Cited by 3 publications

(1 citation statement)

References 16 publications

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“…Another study (Alamir et al, 2023) introduced a modified ARO approach, infused with principles from quantum mechanics, showcasing its efficacy in solving energy management (EM) problems by optimizing benefits and reducing time consumption. Furthermore, (Samal et al, 2023) effectively employed ARO to address economic load dispatch challenges. Despite the notable achievements highlighted in the aforementioned research, ARO does present certain limitations.…”

Section: Introductionmentioning

confidence: 99%

An improved Artificial Rabbit Optimization for structural damage identification

Huu,

Ngoc,

Tien

et al. 2024

Lat. Am. j. solids struct.

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This paper presents an enhanced version of the Artificial Rabbit Optimization (ARO) algorithm designed for identifying structural damage in bridge structures. The original ARO draws inspiration from survival observed in wild rabbits. However, it demands a substantial investment of computational time. Therefore, in this paper, the Improved ARO (IARO) algorithm incorporating elements of the Grey Wolf Optimizer (GWO) through hybridization, is employed to deal with optimization problems. The central concept of this approach involves infusing predator-hunting characteristics into the prey-rabbit during the hunting process, thereby enabling more effective predator evasion. The proposed method is evaluated through a series of simulations related to two real bridges: a simple supported beam structure and a steel truss bridge. The results show a significant improvement in accuracy and efficiency in determining structural damage while considering factors such as damage location, severity, and computation time. These findings underscore the potential of the proposed approach for real-world applications in structural health monitoring and damage detection.

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Section: Introductionmentioning

confidence: 99%

An improved Artificial Rabbit Optimization for structural damage identification

Huu,

Ngoc,

Tien

et al. 2024

Lat. Am. j. solids struct.

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Reinforcement learning guided Spearman dynamic opposite Gradient-based optimizer for numerical optimization and anchor clustering

Sun,

Huo,

Jia

et al. 2023

Journal of Computational Design and Engineering

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As science and technology advance, the need for novel optimization techniques has led to an increase. The recently proposed metaheuristic algorithm, Gradient-based optimizer (GBO), is rooted in the gradient-based Newton's method. GBO has a more concrete theoretical foundation. However, GSR and LEO operators in GBO still have some shortcomings. The insufficient updating method and the simple selection process limit the search performance of the algorithm. In this paper, an improved version is proposed to compensate for the above shortcomings, called RL-SDOGBO. First, during the GSR phase, the Spearman rank correlation coefficient is used to determine weak solutions on which to perform dynamic opposite learning. This operation assists the algorithm to escape from local optima and enhance exploration capability. Second, to optimize the exploitation capability, reinforcement learning is used to guide the selection of solution update modes in the LEO operator. RL-SDOGBO is tested on 12 classical benchmark functions and 12 CEC2022 benchmark functions with seven representative metaheuristics, respectively. The impact of the improvements, the scalability and running time of the algorithm, and the balance of exploration and exploitation are analyzed and discussed. Combining the experimental results and some statistical results, RL-SDOGBO exhibits excellent numerical optimization performance and provides high-quality solutions in most cases. In addition, RL-SDOGBO is also used to solve the anchor clustering problem for small target detection, making it a more potential and competitive option.

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