A modified weighted chimp optimization algorithm for training feed-forward neural network

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.