Harmony search (HS) meta-heuristic algorithm is proposed by [PERSON] et al. [5] after the optimization problem is specified to discover optimum tilt and azimuth angle to maximize extraterrestrial radiation on a collector in China. The results are compared with a reference group which is obtained by the ergodic method conducted in different cities to understand the performance of HS. Additionally, particle swarm optimization (PSO) is used to compare the solution quality with the HS algorithm.  
[PERSON] et al. [6] proposed a genetic algorithm(GA) to obtain the exact solution for SDM parameter identification requiring only some measured points close to a maximum power point (MPP).  
[PERSON] et al. [7] use a symbiotic organisms search algorithm (SOS) to extract parameters from solar cell models. The effectiveness of this model validated by the single diode model, double diode model, and PV module model. In addition, to verify the effectiveness of SOS, five state-of-the-art algorithms including an across neighborhood search (ANS) biogeography-based learning particle swarm optimization (BLPSO), competitive swarm optimizer (CSO), chaotic teaching-learning algorithm (CTLA), and levy flight trajectory-based whale optimization algorithm (LWOA) are used for performance comparison. Comparison on a statistical level is done by the [PERSON]'s rank sum test at a 0.05 confidence level to identify the significance difference between SOS and other compared methods on the same case.  
[PERSON] et al. [8] simulated different dynamic and partial shading conditions based on the PSO evolutionary approach maximum power point tracking (MPPT) algorithm and compared it with classical MPPT methods to investigate conversion efficiency in the conducted scenarios.  
[PERSON] et al. [9] analyzed different approaches in training data set composition for ANN to be used in the physical hybrid method. For ANN, the training algorithm is chosen as Levenberg-Marquardt while the activation function is sigmoid and the number of trials in the ensemble forecast is 40. An additional performance index (envelope-weighted mean absolute error) is proposed to compare results between different approaches.  
[PERSON] et al. [10] perform a comparison among bioinspired algorithms by taking three cases: single diode model, double diode model, and photovoltaic module to predict solar cell and PV module parameters. The Firefly algorithm is chosen for the optimization problem. The results are compared with recent techniques such as the biogeography-based optimization algorithm with mutation strategies (BBO-M), the Levenberg-Marquardt algorithm combined with simulated annealing (LMSA), artificial bee swarm optimization algorithm, artificial bee colony optimization (ABC), hybrid Nelder-Mead and modified particle swarm optimization (NMMPSO), repaired adaptive differential evolution (RADE), chaotic asexual reproduction optimization (CARO) for solar cell single and double diodes; quasi-Newton (Q-N) method and self-organizing migrating algorithm (SOMA) for a-Si:H solar cell and the optimal parameters of Photowatt-PWP 201 are compared with the Newton-Raphson pattern search(PS), genetic algorithm (GA) and simulated annealing algorithm(SA). In conclusion, this Special Issue contains a series of up-to-date research work covering a wide area of application-oriented computational intelligence. This collection of ten papers is highly recommended and will benefit readers in various aspects dealing with solar power systems.  
We would like to thank all authors, the many dedicated referees, the editor team of _Applied Sciences_, and especially [PERSON] (Assistant Managing Editor), for their valuable contributions, making this special issue a success.  
The authors declare no conflict of interest.