Comparative Analysis of Intelligent Solutions Searching Algorithms of Particle Swarm Optimization and Ant Colony Optimization for Artificial Neural Networks Target Dataset

Alfa, Abraham Ayegba; Misra, Sanjay; Abayomi‐Alli, Adebayo; Arogundade, Oluwasefunmi ‘Tale; Ozik, Jonathan; Ahuja, Ravin

doi:10.1007/978-981-16-0733-2_32

Cited by 2 publications

(1 citation statement)

References 17 publications

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“…• Ant Colony Optimization-Recent Variants, Applications, and Perspectives: The work of Misra & Chakraborty (2024) offers an exhaustive examination of Ant Colony Optimization (ACO) algorithms and their deployment across a spectrum of engineering challenges [Alfa et al, 2021, Alfa et al, 2020. The authors elucidate the probabilistic essence of ACO, a process wherein artificial ants alter pheromone trails, thereby steering subsequent ants towards superior solutions.…”

Section: Data Colony Optimization (Dco)mentioning

confidence: 99%

Radar Transmogrification Technology: Support for Unmanned System

Pandey

Sharma²

2019

2019 Amity International Conference on Artificial Intelligence (AICAI)

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The paper introduces D-CODE, a new framework blending Data Colony Optimization (DCO) algorithms inspired by biological colonies' collective behaviors with Dynamic Efficiency (DE) models for real-time adaptation. DCO utilizes metaheuristic strategies from ant colonies, bee swarms, and fungal networks to efficiently explore complex data landscapes, while DE enables continuous resource recalibration and process adjustments for optimal performance amidst changing conditions. Through a mixedmethods approach involving simulations and case studies, D-CODE outperforms traditional techniques, showing improvements of 3-4% in solution quality, 2-3 times faster convergence rates, and up to 25% higher computational efficiency. The integration of DCO's robust optimization and DE's dynamic responsiveness positions D-CODE as a transformative paradigm for intelligent systems design, with potential applications in operational efficiency, decision support, and computational intelligence, supported by empirical validation and promising outcomes.

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Section: Data Colony Optimization (Dco)mentioning

confidence: 99%

Radar Transmogrification Technology: Support for Unmanned System

Pandey

Sharma²

2019

2019 Amity International Conference on Artificial Intelligence (AICAI)

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Prediction of Photovoltaic Panels Output Performance Using Artificial Neural Network

Loukriz

Saigaa

Kherbachi

et al. 2022

International Journal of Energy Optimization and Engineering

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To ensure the safe and stable operation of solar photovoltaic system-based power systems, it is essential to predict the PV module output performance under varying operating conditions. In this paper, the interest is to develop an accurate model of a PV module in order to predict its electrical characteristics. For this purpose, an artificial neural network (ANN) based on the backpropagation algorithm is proposed for the performance prediction of a photovoltaic module. In this modeling approach, the temperature and illumination are taken as inputs and the current of the mathematical model as output for the learning of the ANN-PV-Panel. Simulation results showing the performance of the ANN model in obtaining the electrical properties of the chosen PV panel, including I–V curves and P–V curves, in comparison with the mathematical model performance are presented and discussed. The given results show that the error of the maximum power is very small while the current error is about 10-8, which means that the obtained model is able to predict accurately the outputs of the PV panel.

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Comparative Analysis of Intelligent Solutions Searching Algorithms of Particle Swarm Optimization and Ant Colony Optimization for Artificial Neural Networks Target Dataset

Cited by 2 publications

References 17 publications

Radar Transmogrification Technology: Support for Unmanned System

Radar Transmogrification Technology: Support for Unmanned System

Prediction of Photovoltaic Panels Output Performance Using Artificial Neural Network

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