Kaya Ebubekir, Baştemur Kaya Ceren, Bendeş Emre, Atasever Sema, Öztürk Başak, Yazlık Bilgin
Department of Computer Engineering, Engineering Architecture Faculty, Nevsehir Haci Bektas Veli University, Nevşehir 50300, Türkiye.
Department of Computer Technologies, Nevsehir Vocational School, Nevsehir Haci Bektas Veli University, Nevşehir 50300, Türkiye.
Biomimetics (Basel). 2023 Sep 1;8(5):402. doi: 10.3390/biomimetics8050402.
One of the most used artificial intelligence techniques for maximum power point tracking is artificial neural networks. In order to achieve successful results in maximum power point tracking, the training process of artificial neural networks is important. Metaheuristic algorithms are used extensively in the literature for neural network training. An important group of metaheuristic algorithms is swarm-intelligent-based optimization algorithms. In this study, feed-forward neural network training is carried out for maximum power point tracking by using 13 swarm-intelligent-based optimization algorithms. These algorithms are artificial bee colony, butterfly optimization, cuckoo search, chicken swarm optimization, dragonfly algorithm, firefly algorithm, grasshopper optimization algorithm, krill herd algorithm, particle swarm optimization, salp swarm algorithm, selfish herd optimizer, tunicate swarm algorithm, and tuna swarm optimization. Mean squared error is used as the error metric, and the performances of the algorithms in different network structures are evaluated. Considering the results, a success ranking score is obtained for each algorithm. The three most successful algorithms in both training and testing processes are the firefly algorithm, selfish herd optimizer, and grasshopper optimization algorithm, respectively. The training error values obtained with these algorithms are 4.5 × 10, 1.6 × 10, and 2.3 × 10, respectively. The test error values are 4.6 × 10, 1.6 × 10, and 2.4 × 10, respectively. With these algorithms, effective results have been achieved in a low number of evaluations. In addition to these three algorithms, other algorithms have also achieved mostly acceptable results. This shows that the related algorithms are generally successful ANFIS training algorithms for maximum power point tracking.
用于最大功率点跟踪的最常用人工智能技术之一是人工神经网络。为了在最大功率点跟踪中取得成功的结果，人工神经网络的训练过程很重要。元启发式算法在文献中被广泛用于神经网络训练。元启发式算法的一个重要类别是基于群体智能的优化算法。在本研究中，使用13种基于群体智能的优化算法对前馈神经网络进行训练以实现最大功率点跟踪。这些算法分别是人工蜂群算法、蝴蝶优化算法、布谷鸟搜索算法、鸡群优化算法、蜻蜓算法、萤火虫算法、蚱蜢优化算法、磷虾群算法、粒子群优化算法、樽海鞘群算法、自私羊群优化器、被囊动物群算法和金枪鱼群优化算法。均方误差用作误差度量，并评估算法在不同网络结构中的性能。考虑结果，为每种算法获得一个成功排名分数。在训练和测试过程中最成功的三种算法分别是萤火虫算法、自私羊群优化器和蚱蜢优化算法。用这些算法获得的训练误差值分别为4.5×10、1.6×10和2.3×10。测试误差值分别为4.6×10、1.6×10和2.4×10。使用这些算法，在较少的评估次数下取得了有效的结果。除了这三种算法外，其他算法也大多取得了可接受的结果。这表明相关算法通常是用于最大功率点跟踪的成功的自适应神经模糊推理系统训练算法。
