Adaptive slime mould algorithm for optimal design of photovoltaic models

Solar energy is becoming more popular as it is a clean source of electricity. The design of photovoltaic (PV) cells has therefore captivated experts worldwide. The two key issues are the lack of an excellent model to define solar cells and the lack of data regarding PV cells. This scenario even impacts solar module performance (panels). The behavior of solar cells is described by the current versus voltage. Considering these values, the design challenge entails solving complicated nonlinear multimodal objectives. Different methods to figure out the parameters of the PV cells and panels have been suggested. They do not come up with the best solutions most of the time. Hence, a powerful and reliable optimizer is needed to derive the optimal parameters of these models. To this end, this study has developed an adaptive slime mould algorithm (ASMA) as a robust and precise optimization method. To implement the ASMA, four improvements are proposed: (1) a trigonometric-based mutation and a double-based best mutation are introduced to promote the global and local search; (2) a suitable mechanism to adaptively select the control parameters of the SMA; (3) a local escaping strategy; (4) an opposition-based learning operator to improve the best solution. The ASMA is employed to derive optimal parameters of PV models and assessed utilizing a total number of eight well-known optimization algorithms. The findings show that the ASMA is very competitive in terms of accuracy and convergence speed and that this is supported by a wealth of evidence. As a result, when assessing the parameters of the PV model, ASMA is a very efficient and robust optimizer. The source codes of the proposed ASMA will be uploaded for the public at and .