Department of Mathematics, Faculty of Science, Suez Canal University, Ismailia, Egypt.
Faculty of Computers and Informatics, Suez Canal University, Ismailia, Egypt.
PLoS One. 2023 Mar 28;18(3):e0282514. doi: 10.1371/journal.pone.0282514. eCollection 2023.
Swarm intelligence algorithms (SI) have an excellent ability to search for the optimal solution and they are applying two mechanisms during the search. The first mechanism is exploration, to explore a vast area in the search space, and when they found a promising area they switch from the exploration to the exploitation mechanism. A good SI algorithm can balance the exploration and the exploitation mechanism. In this paper, we propose a modified version of the chimp optimization algorithm (ChOA) to train a feed-forward neural network (FNN). The proposed algorithm is called a modified weighted chimp optimization algorithm (MWChOA). The main drawback of the standard ChOA and the weighted chimp optimization algorithm (WChOA) is they can be trapped in local optima because most of the solutions update their positions based on the position of the four leader solutions in the population. In the proposed algorithm, we reduced the number of leader solutions from four to three, and we found that reducing the number of leader solutions enhances the search and increases the exploration phase in the proposed algorithm, and avoids trapping in local optima. We test the proposed algorithm on the Eleven dataset and compare it against 16 SI algorithms. The results show that the proposed algorithm can achieve success to train the FNN when compare to the other SI algorithms.
群体智能算法（SI）具有出色的搜索最优解的能力，它们在搜索过程中应用了两种机制。第一种机制是探索，以在搜索空间中探索广阔的区域，当它们发现有希望的区域时，它们会从探索机制切换到利用机制。一个好的 SI 算法可以平衡探索和利用机制。在本文中，我们提出了一种改进的类人猿优化算法（ChOA）来训练前馈神经网络（FNN）。所提出的算法称为改进加权类人猿优化算法（MWChOA）。标准 ChOA 和加权类人猿优化算法（WChOA）的主要缺点是它们可能陷入局部最优，因为大多数解决方案根据种群中四个领导者解决方案的位置更新其位置。在提出的算法中，我们将领导者解决方案的数量从四个减少到三个，我们发现减少领导者解决方案的数量可以增强搜索并增加算法的探索阶段，从而避免陷入局部最优。我们在 Eleven 数据集上测试了所提出的算法，并将其与 16 个 SI 算法进行了比较。结果表明，与其他 SI 算法相比，所提出的算法在训练 FNN 方面可以取得成功。
