Autonomous Population Regulation Using a Multi-Agent System in a Prey–Predator Model That Integrates Cellular Automata and the African Buffalo Optimization Metaheuristic

Almonacid, Boris; Aspée, Fabián; Yimes, Francisco

doi:10.3390/a12030059

Cited by 2 publications

(2 citation statements)

References 49 publications

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“…Equation ( 8) provides the equation for this categorization method. 𝑦𝑦 = 𝐿𝐿𝑁𝑁(𝑍𝑍 𝑙𝑙 0 ) (8) ABO for Hyper Parameter Tuning An method that falls under the category of stochastic metaheuristics and is part of the population algorithms branch is the African buffalo optimisation [28]. The ABO metaheuristic takes its cues from the way herds of African buffalo behave during migration.…”

Section: ) Classification Layermentioning

confidence: 99%

Designing a Smart Agri-Crop Framework on Cotton Production using ABO Optimized Vision Transformer Model

et al. 2024

JMC

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Due to its widespread cultivation and large yields by most farmers, cotton is another vital cash crop. However, a number of illnesses lower the quantity and quality of cotton harvests, which causes a large loss in output. Early diagnosis detection of these illnesses is essential. This study employs a thorough methodology to solve the crucial job of cotton leaf disease identification by utilising the "Cotton-Leaf-Infection" dataset. Preprocessing is the first step, in which noise is removed from the dataset using a Prewitt filter, which improves the signal-to-noise ratio. Next, a state-of-the-art process for image classification errands called Vision Transformer (ViT) model is used to carry out the disease categorization. Additionally, the study presents the African Buffalo Optimisation (ABO) method, which optimises weight during the classification procedure. The African buffalo's cooperative behaviour served as the model's inspiration for the ABO algorithm, which is remarkably effective at optimising the model's parameters. By integrating ABO, the problems caused by the dynamic character of real-world agricultural datasets are addressed and improved model resilience and generalisation are facilitated. The suggested ViT-based categorization model shows remarkable effectiveness, with a remarkable 99.3% accuracy rate. This performance is higher than current models.

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Section: ) Classification Layermentioning

confidence: 99%

Designing a Smart Agri-Crop Framework on Cotton Production using ABO Optimized Vision Transformer Model

et al. 2024

JMC

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“…One study delves into an NP-hard multi-period production distribution problem, employing a memetic algorithm with population management to simultaneously handle production and distribution decisions, achieving significant savings compared to two-phase methods [ 31 ]. Another investigation focuses on a dynamic prey–predator spatial model, introducing the African buffalo optimization metaheuristic and employing autonomous multi-agents to regulate buffalo populations, achieving a balanced coexistence of prey and predators [ 32 ].…”

Section: Related Workmentioning

confidence: 99%

Dynamic Population on Bio-Inspired Algorithms Using Machine Learning for Global Optimization

Caselli,

Soto,

Crawford

et al. 2023

Biomimetics

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In the optimization field, the ability to efficiently tackle complex and high-dimensional problems remains a persistent challenge. Metaheuristic algorithms, with a particular emphasis on their autonomous variants, are emerging as promising tools to overcome this challenge. The term “autonomous” refers to these variants’ ability to dynamically adjust certain parameters based on their own outcomes, without external intervention. The objective is to leverage the advantages and characteristics of an unsupervised machine learning clustering technique to configure the population parameter with autonomous behavior, and emphasize how we incorporate the characteristics of search space clustering to enhance the intensification and diversification of the metaheuristic. This allows dynamic adjustments based on its own outcomes, whether by increasing or decreasing the population in response to the need for diversification or intensification of solutions. In this manner, it aims to imbue the metaheuristic with features for a broader search of solutions that can yield superior results. This study provides an in-depth examination of autonomous metaheuristic algorithms, including Autonomous Particle Swarm Optimization, Autonomous Cuckoo Search Algorithm, and Autonomous Bat Algorithm. We submit these algorithms to a thorough evaluation against their original counterparts using high-density functions from the well-known CEC LSGO benchmark suite. Quantitative results revealed performance enhancements in the autonomous versions, with Autonomous Particle Swarm Optimization consistently outperforming its peers in achieving optimal minimum values. Autonomous Cuckoo Search Algorithm and Autonomous Bat Algorithm also demonstrated noteworthy advancements over their traditional counterparts. A salient feature of these algorithms is the continuous nature of their population, which significantly bolsters their capability to navigate complex and high-dimensional search spaces. However, like all methodologies, there were challenges in ensuring consistent performance across all test scenarios. The intrinsic adaptability and autonomous decision making embedded within these algorithms herald a new era of optimization tools suited for complex real-world challenges. In sum, this research accentuates the potential of autonomous metaheuristics in the optimization arena, laying the groundwork for their expanded application across diverse challenges and domains. We recommend further explorations and adaptations of these autonomous algorithms to fully harness their potential.

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Autonomous Population Regulation Using a Multi-Agent System in a Prey–Predator Model That Integrates Cellular Automata and the African Buffalo Optimization Metaheuristic

Cited by 2 publications

References 49 publications

Designing a Smart Agri-Crop Framework on Cotton Production using ABO Optimized Vision Transformer Model

Designing a Smart Agri-Crop Framework on Cotton Production using ABO Optimized Vision Transformer Model

Dynamic Population on Bio-Inspired Algorithms Using Machine Learning for Global Optimization

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