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Chimp optimization algorithm (ChOA) is a robust nature-inspired technique, which was recently proposed for addressing real-world challenging engineering problems. Due to the novelty of the ChOA, there is room for its improvement. Recognition and classification of marine mammals using artificial neural networks (ANNs) are high-dimensional challenging problems. In order to address this problem, this paper proposed the using of ChOA as ANN's trainer. However, evolving ANNs using metaheuristic algorithms suffers from high complexity and processing time. In order to address this shortcoming, this paper proposes the fuzzy logic to adjust the ChOA's control parameters (Fuzzy-ChOA) for tuning the relationship between exploration and exploitation phases. In this regard, we collect underwater marine mammals sounds and then produce an experimental dataset. After pre-processing and feature extraction, the ANN is used as a classifier. Besides, for having a fair comparison, we used a benchmark audio database of marine mammals. The comparison algorithms include ChOA, coronavirus optimization algorithm, harris hawks optimization, black widow optimization algorithm, Kalman filter benchmark algorithms, and also comparative benchmarks include convergence speed, local optimal avoidance ability, classification rate, and receiver operating characteristics (ROC). The simulation results show that the proposed fuzzy model can tune the boundary between the exploration and extraction phases. The convergence curve and ROC confirm that the convergence rate and performance of the designed recognizer are better than benchmark algorithms.

Overfitting has been always considered as a challenging problem in designing and training of ensemble classifiers. Obviously, the use of complex multiple classifiers may increase the success of ensemble classifier in feature space division with intertwined data and also may decrease the training error to minimum value. However, this success does not exist on the test data. Ensemble classifiers are more prone to overfitting than single classifiers because ensemble classifiers have been formed of several base classifiers and overfitting occurrence in each base classifier can transfer the problem to the final decision of the ensemble. In this paper, after quantitative and qualitative analysis of overfitting, a solution for improving overfitting is proposed by using heuristic algorithms. In this way, Multi-Objective Inclined Planes Optimization (MOIPO) and Multi-Objective ParticleSwarm Optimization (MOPSO) are used and their results are compared with each other. Simulation results show that the simultaneous minimization of ensemble size and error rate in the training phase, can lead to a significant reduction in the amount of overfitting. In fact, with this approach in the training phase, the ensemble classifier is required to minimize the error with the most simple and minimum number of base classifiers and therefore overfitting is prevented. However, previous researches related to overfitting have ignored the ensemble size as an objective function.