Estimating one-diode-PV model using autonomous groups particle swarm optimization

Abstract

In this paper, the one-diode model of a photovoltaic PV solar cell (PVSC) is estimated for an experimental characteristic curves data by using a recently proposed version of the Particle Swarm Optimization (PSO) algorithm, which is known as the Autonomous Groups Particles Swarm Optimization (PSOAG). This meta-heuristic algorithm is used to identify the model of the PVSC. The PSOAG divides the particles into groups and then, uses different functions to tune the social and cognitive parameters of these groups. This is done to show the individuals’ diversity inside the swarm. Although, these individuals do their duties as part of the society, they are not similar in terms of intelligence and ability. By using these groups, the performance of the PSO is improved in terms of convergence rate and escaping the local minima/maxima. Six versions of PSOAG algorithms were developed in this work. Therefore, nine versions of PSOAG, including these six algorithms and three newly developed PSOAG reported previously, will be used in this research to cover more social’s behaviors. The results are compared to the original PSO and other versions of PSO like conventional and Asymmetric Time-varying Accelerated Coefficient PSOs, and the improved PSO. The result shows that the proposed methods improve the performance by up to 14% in terms of root mean squared error and maximum absolute error, and by up to 20% in term of convergence rate, when these were compared to the best results obtained from the other algorithms.

Face mask detection using machine learning

“…With the use of machine learning (ML), a subfield of artificial intelligence, software systems may anticipate results more precisely without explicit programming. It functions by building a mathematical model that is trained using practice data to learn and develop [19][20][21][22][23][24][25][26][27][28][29][30]. Then, this model is used to forecast the results of testing sets.…”

Path planning and obstacle avoidance utilizing chameleon swarm algorithm

“…The chameleon is a kind of reptile renowned for its extraordinary capacity to alter its color to blend into its environment [1]. The Chameleon Swarm Algorithm (CSA) similar to PSO [2][3][4][5][6] is a metaheuristic nature-inspired optimization method created to address engineering optimization issues, which was inspired by the chameleon's hunting activity [7]. There are three key phases to the algorithm: tracking, pursuit, and attack.…”

Studying the gender effect on apparent mass during whole-body vibration using ANN

“…Moreover, the usage of the machine learning could you help losing weight and improve the life quality [13][14][15][16][17][18][19][20][21][22][23]. Moreover, metaheuristic algorithms, integrated with machine learning techniques [24][25][26][27][28][29][30][31][32][33][34][35][36], can optimize the selection of input parameters for WBV studies on weight loss and quality of life improvements, overcoming challenges related to standardized protocols and diverse parameter settings, and providing more consistent and reliable outcomes [37][38][39][40][41][42][43][44][45][46][47][48][49]. In this work, the work of [1,12] is extended to cover the effect of the human gender on the apparent mass.…”