Abstract
The greater the demand for energy, the more important it is to improve and develop permanent energy sources, because of their advantages over non-renewable energy sources. With the development of artificial intelligence algorithms and the presence of so many data, the evolution of simulation models has increased. In this research, an improvement to one recent optimization algorithm called the artificial hummingbird algorithm (AHA) is proposed. An adaptive opposition approach is suggested to select whether or not to use an opposition-based learning (OBL) method. This improvement is developed based on adding an adaptive updating mechanism to enable the original algorithm to obtain more accurate results with more complex problems, and is called the adaptive opposition artificial hummingbird algorithm (AOAHA). The proposed AOAHA was tested on 23 benchmark functions and compared with the original algorithm and other recent optimization algorithms such as supply–demand-based optimization (SDO), wild horse optimizer (WHO), and tunicate swarm algorithm (TSA). The proposed algorithm was applied to obtain accurate models for solar cell systems, which are the basis of solar power plants, in order to increase their efficiency, thus increasing the efficiency of the whole system. The experiments were carried out on two important models—the static and dynamic models—so that the proposed model would be more representative of real systems. Two applications for static models have been proposed: In the first application, the AOAHA satisfies the best root-mean-square values (0.0009825181). In the second application, the performance of the AOAHA is satisfied in all variable irradiance for the system. The results were evaluated in more than one way, taking into account the comparison with other modern and powerful optimization techniques. Improvement showed its potential through its satisfactory results in the tests that were applied to it.
Highlights
Introduction iationsThe remarkable development of communication systems and easy access to information are the main factors in the development of artificial intelligence algorithms that, in turn, look at the utilization of these data and produce results that help improve the performance of multiple systems [1]
The artificial hummingbird algorithm (AHA) is an optimization technique inspired by the foraging and flight of humThe AHA is an optimization technique inspired by the foraging and flight of hummingbirds, as presented in [27]
The proficiency and performance of the proposed adaptive opposition artificial hummingbird algorithm (AOAHA) technique were evaluated based on several benchmark functions, using he statistical measurements such as best values, mean values, median values, worst values, and standard deviation (STD) for the best solutions acquired by the AOAHA and the other state-of-the-art optimization algorithms
Summary
The remarkable development of communication systems and easy access to information are the main factors in the development of artificial intelligence algorithms that, in turn, look at the utilization of these data and produce results that help improve the performance of multiple systems [1] This improvement has had the effect of increasing the efficiency of these systems, thereby increasing the economic return and guiding the vision for the future. Energy sources are some of the most important systems that researchers have been concerned with developing and making the best use of, because of their economic and strategic value to the whole world This has had a severe impact in increasing the search space around the application of artificial intelligence algorithms to obtain ideal models for these systems, so that developers can study the performance of these systems in the laboratory and, save on high manufacturing costs [2].
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