A Comprehensive Survey on Arithmetic Optimization Algorithm

Dhal, Krishna Gopal; Sasmal, Buddhadev; Das, Arunita; Ray, Swarnajit; Rai, Rebika

doi:10.1007/s11831-023-09902-3

Cited by 21 publications

(3 citation statements)

References 161 publications

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“…If r is greater than or equal to 0.5, the division search technique is used, otherwise, the multiplication search approach is used. During the exploration phase, the range of the candidate pool is managed using MOP [36], a mathematical probability optimizer. In Eq.…”

Section: Self-adaptive Strategymentioning

confidence: 99%

SGEO: Equalization Optimizer with Hybrid Learning Strategies for UAV Path Planning in Complex 3D Environments

Zheng,

He,

2024

Preprint

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Unmanned Aerial Vehicle (UAV) route planning is an intricate issue that requires the comprehensive consideration of multiple factors and the combination of suitable strategies to achieve efficient and safe flight paths. Its purpose is to plan a suitable navigational path for the drone to achieve a specific task or avoid obstacles. In practical applications, UAVs are usually required to accomplish tasks in a variety of complex environments, resulting in the feasible flight paths will be reduced and path planning for UAVs becomes difficult. Accordingly, we design a simple yet useful planner, called Self-adaptive Golden Equilibrium Optimization algorithm (SGEO). The proposed algorithm combines the new self-adaptive approach, the acceleration control factor and the Golden-SA strategy in order to balance the exploitation and exploration capabilities. The novel self-adaptive strategy is used to expand the global search range of the algorithm and enhance its ability for global exploration; the acceleration control factor is introduced to control the parameters a1 and a2 and improve the convergence ability of the algorithm; the Golden-SA strategy helps the candidate particles achieve better balance between different dimensions based on the golden ratio and sine function, this enables the particles to explore the search space more comprehensively. The limitations in UAV route planning are translated into the objective function, and four more algorithms are introduced to compare with SGEO in two different circumstances. The simulation findings reveal that SGEO excels at three-dimensional path planning for UAV in complicated environments.

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Section: Self-adaptive Strategymentioning

confidence: 99%

SGEO: Equalization Optimizer with Hybrid Learning Strategies for UAV Path Planning in Complex 3D Environments

Zheng,

He,

2024

Preprint

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“…It draws inspiration from the four basic arithmetic operations, consisting of exploration and exploitation phases. Global exploration is achieved using multiplication and division operations, while local exploitation uses addition and subtraction operations [11] [13]. AOA has advantages such as high precision, strong adaptability and simple parameters.…”

Section: Introductionmentioning

confidence: 99%

Multi-threshold image segmentation research based on improved enhanced arithmetic optimization algorithm

Li,

Zhu,

et al. 2024

SIViP

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To address the disadvantages of low efficiency, slow convergence speed and easy to fall into local optimal of arithmetic optimization algorithm (AOA), this paper proposes an improved arithmetic optimization algorithm, called IAOA, and applies it to multi-threshold image segmentation processing.Compared with the original algorithm, the improvements are: first, inertia weight and cosine factor are introduced to AOA to enhance algorithm stability; then chaotic mapping is embedded in the algorithm selection stage to accelerate convergence speed; finally, Cauchy mutation and reverse learning strategies are introduced to avoid the algorithm falling into local optimal. This paper uses 23 benchmark functions in CEC-2005 to test the performance of the IAOA, and selects 8 commonly used image segmentation performance indicators and 3 image quality indicators to detect the image segmentation effect of the IAOA. In addition, several classic metaheuristic algorithms are selected for comparison. The results show that compared with other algorithms, the IAOA has stronger robustness and better segmentation performance.

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“…Swarm intelligence algorithms consist of two main phases, namely exploration and exploitation [31][32][33][34]. The purpose of exploration is to search the regions where the global optimum may exist.…”

Section: Introductionmentioning

confidence: 99%

Fault Prediction of Control Clusters Based on an Improved Arithmetic Optimization Algorithm and BP Neural Network

Gao

Zhuang

2023

Mathematics

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Higher accuracy in cluster failure prediction can ensure the long-term stable operation of cluster systems and effectively alleviate energy losses caused by system failures. Previous works have mostly employed BP neural networks (BPNNs) to predict system faults, but this approach suffers from reduced prediction accuracy due to the inappropriate initialization of weights and thresholds. To address these issues, this paper proposes an improved arithmetic optimization algorithm (AOA) to optimize the initial weights and thresholds in BPNNs. Specifically, we first introduced an improved AOA via multi-subpopulation and comprehensive learning strategies, called MCLAOA. This approach employed multi-subpopulations to effectively alleviate the poor global exploration performance caused by a single elite, and the comprehensive learning strategy enhanced the exploitation performance via information exchange among individuals. More importantly, a nonlinear strategy with a tangent function was designed to ensure a smooth balance and transition between exploration and exploitation. Secondly, the proposed MCLAOA was utilized to optimize the initial weights and thresholds of BPNNs in cluster fault prediction, which could enhance the accuracy of fault prediction models. Finally, the experimental results for 23 benchmark functions, CEC2020 benchmark problems, and two engineering examples demonstrated that the proposed MCLAOA outperformed other swarm intelligence algorithms. For the 23 benchmark functions, it improved the optimal solutions in 16 functions compared to the basic AOA. The proposed fault prediction model achieved comparable performance to other swarm-intelligence-based BPNN models. Compared to basic BPNNs and AOA-BPNNs, the MCLAOA-BPNN showed improvements of 2.0538 and 0.8762 in terms of mean absolute percentage error, respectively.

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A Comprehensive Survey on Arithmetic Optimization Algorithm

Cited by 21 publications

References 161 publications

SGEO: Equalization Optimizer with Hybrid Learning Strategies for UAV Path Planning in Complex 3D Environments

SGEO: Equalization Optimizer with Hybrid Learning Strategies for UAV Path Planning in Complex 3D Environments

Multi-threshold image segmentation research based on improved enhanced arithmetic optimization algorithm

Fault Prediction of Control Clusters Based on an Improved Arithmetic Optimization Algorithm and BP Neural Network

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