A hybrid firefly and particle swarm optimization algorithm with local search for the problem of municipal solid waste collection: a real-life example

Kaya, Serkan

doi:10.1007/s00521-022-08173-6

Cited by 3 publications

(1 citation statement)

References 115 publications

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“…In HF&PSO, the movements of the fireflies are governed by FA, while the particle updating mechanism of PSO is utilized. This allows for a better solution to the problem being optimized by enabling fireflies to attract each other and particles to follow the optimal solution [55].…”

Section: Hybrid Firefly-particle Swarm Optimization Algorithmmentioning

confidence: 99%

Exploring solar energy systems: A comparative study of optimization algorithms, MPPTs, and controllers

Güven

2024

IET Control Theory & Appl

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This study elucidates the use of optimization algorithms to identify the controller parameters employed in adjusting the current and voltage values of loads powered by solar energy systems and battery groups. Parameters for these controllers were independently derived using a combination of ant colony optimization with Levy flight, hybrid firefly‐particle swarm optimization, hybrid gravitation search algorithm‐particle swarm optimization, alongside the implementation of Jaya and whale optimization algorithms. The results from each method were juxtaposed for thorough analysis. In addition, three distinct Maximum Power Point Tracker (MPPT) algorithms were employed in the system: perturbation and observation, open circuit voltage, and incremental conductance (IC). To assess the system’s adaptability to real‐world conditions, it was tested against varying temperatures and sunlight levels. Moreover, potential changes in the loads were considered by varying the load. The efficacy of the controllers was examined by altering both the environment and load. The effectiveness of the controllers was examined by referring to the integral of time‐weighted absolute error value. The system was simulated using MATLAB/Simulink software. This study demonstrates that the fractional‐order PID controller achieves the most effective results, the Jaya algorithm provides the best controller parameters, and the IC technique exhibits the highest performance in MPPT.

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Section: Hybrid Firefly-particle Swarm Optimization Algorithmmentioning

confidence: 99%

Exploring solar energy systems: A comparative study of optimization algorithms, MPPTs, and controllers

Güven

2024

IET Control Theory & Appl

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Evaluating the performance of metaheuristic-tuned weight agnostic neural networks for crop yield prediction

Jovanovic,

Zivkovic,

Bacanin

et al. 2024

Neural Comput & Applic

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This study explores crop yield forecasting through weight agnostic neural networks (WANN) optimized by a modified metaheuristic. WANNs offer the potential for lighter networks with shared weights, utilizing a two-layer cooperative framework to optimize network architecture and shared weights. The proposed metaheuristic is tested on real-world crop datasets and benchmarked against state-of-the-art algorithms using standard regression metrics. While not claiming WANN as the definitive solution, the model demonstrates significant potential in crop forecasting with lightweight architectures. The optimized WANN models achieve a mean absolute error (MAE) of 0.017698 and an R-squared ($$R^2$$ R 2 ) score of 0.886555, indicating promising forecasting performance. Statistical analysis and Simulator for Autonomy and Generality Evaluation (SAGE) validate the improvement significance and feature importance of the proposed approach.

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Modeling and evaluating costs and greenhouse gas emissions in solid waste management based on system dynamics in a mega-city: The case of Xi'an

Dou,

Li,

et al. 2024

Journal of Cleaner Production

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A hybrid firefly and particle swarm optimization algorithm with local search for the problem of municipal solid waste collection: a real-life example

Cited by 3 publications

References 115 publications

Exploring solar energy systems: A comparative study of optimization algorithms, MPPTs, and controllers

Exploring solar energy systems: A comparative study of optimization algorithms, MPPTs, and controllers

Evaluating the performance of metaheuristic-tuned weight agnostic neural networks for crop yield prediction

Modeling and evaluating costs and greenhouse gas emissions in solid waste management based on system dynamics in a mega-city: The case of Xi'an

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