This study presents the K-means clustering-based grey wolf optimizer, a new algorithm intended to improve the optimization capabilities of the conventional grey wolf optimizer in order to address the problem of data clustering. The process that groups si
It is not a good solution to the single of the diversity of the gray wolf population. In Ramana et al. [26], they proposed an improved chaotic gray wolf optimization algorithm, they incorporated five different chaotic mappings into the gray wolf optimization algorithm, and achieved relatively ...
The grey wolf position is randomly generated within a given range. Then, the grey wolf optimization algorithm based on levy flight and mutation mechanism is used to iteratively calculate the optimal position, which is the internal parameters of cameras. The two groups of experimental data were ...
Considering the source size and location, line loading limits, generator capacity, and bus parameters, the Grey Wolf optimization algorithm determines the optimum solution for the capacity expansion. Thus, an effective method is proposed for power system planning. According to the optimization results ...
38 proposed a strengthened hierarchy mechanism, where each grey wolf updates its position based on its social rank, thereby enhancing optimization performance. (3) The balance between exploration and exploitation39. A robust algorithm must strike a balance between exploration and exploitation40. ...
(ii) the time response function of the model and the restored values are deduced in detail; (iii) the model parameters of the novel model are determined by a well-known algorithm, namely, particle swarm optimization (PSO) [29]; (iv) two examples and a real application are used to ...
The grey wolf optimization (GWO) algorithm is implemented to determine the optimal switching angles of the proposed control scheme. The Total Harmonic Distortion (THD) objective is selected to be the fitness function to be minimized for improving the quality of the output waveforms. For verification...
Multi-objective Grey Wolf Optimization algorithm was proposed on this basis in 2015. Its updated formula is: (20)Dik=|C∗Xpk−Xik| (21)Xik+1=Xpk−A∗Dik In Eqs. (20) and (21), Xp is the current location of the prey; Xi is the location of the grey wolf particles; C, A...
Metaheuristic optimization techniques – moth-flame optimization, salp swarm algorithm, improved grey wolf optimizer, and multi-verse optimizer – are employed to find the best solution for the generation cost, losses, and emissions. Various scenarios are examined to approve the ability of the ...
multi-modal optimization landscapes. Therefore, the algorithm might find a solution quickly, but it might not be the best possible solution; (ii) Over iterations, the wolf agents in GWO can cluster around specific regions of the search space, leading to a lack of diversity. This clustering can...