An Improved Ant Colony Algorithm Based on Levy Flight Distribution

Bansal, Sandhya; Goel, Rajeev K.; Maini, Raman

doi:10.37418/amsj.9.6.70

Cited by 1 publication

(3 citation statements)

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“…In ACO, a complete tour is formed for the first ant before initiating the tour for the second ant. At the inception of the algorithm, the initial pheromones deposited on the connections are set to a small value, such as τ 0 > 0 [73].…”

Section: Methodsmentioning

confidence: 99%

“…Consequently, the pheromone on all connections evaporates, as defined by Equation (44). This process of reducing the number of pheromones on all possible paths is referred to as local updating [73].

\begin{equation}{\tau }_{ij} \left( {t + 1} \right) = \left( {1 - \xi } \right)\ {\tau }_{ij}\left( t \right)\end{equation}

where ξ is the pheromone evaporation factor.…”

Section: Methodsmentioning

confidence: 99%

Section: Ant Colony Optimization-levy Flight Algorithmmentioning

confidence: 99%

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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: Methodsmentioning

confidence: 99%