The breast cancer dataset’s statistical results.

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.群智能算法（Swarm Intelligence, SI）具备优异的最优解搜索能力，其搜索过程依托两大核心机制：其一为探索机制，用于遍历搜索空间的广阔区域；当寻得具有潜力的搜索区域后，算法便会从探索模式切换至开发机制。一款优秀的群智能算法需能够平衡探索与开发两大机制。本文针对黑猩猩优化算法（Chimp Optimization Algorithm, ChOA）提出改进版本，用于训练前馈神经网络（Feedforward Neural Network, FNN），所提算法被命名为改进加权黑猩猩优化算法（Modified Weighted Chimp Optimization Algorithm, MWChOA）。标准黑猩猩优化算法与加权黑猩猩优化算法（Weighted Chimp Optimization Algorithm, WChOA）的主要缺陷在于，种群内多数解的位置更新均基于种群中4个领导者解的位置，因此易陷入局部最优。在本文所提算法中，我们将领导者解的数量从4个缩减至3个，实验发现此举能够强化搜索过程，提升算法探索阶段的性能，同时避免陷入局部最优。我们将所提算法在Eleven数据集上开展测试，并与16种群智能算法进行对比，实验结果表明，相较于其他群智能算法，所提算法可有效完成前馈神经网络的训练任务。