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Fuzzy-ChOA: an improved chimp optimization algorithm for marine mammal classification using artificial neural

Abbas Saffari1, Mohammad Khishe2, Seyed-Hamid Zahiri1

  • 1Department of Electrical Engineering, University of Birjand, Birjand, Iran.

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Summary
This summary is machine-generated.

This study enhances marine mammal recognition by improving the Chimp Optimization Algorithm (ChOA) with fuzzy logic. The Fuzzy-ChOA significantly boosts the performance of Artificial Neural Networks (ANNs) for accurate classification.

Keywords:
Chimp optimization algorithmClassificationFuzzy systemNeural network. Marine mammals

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Area of Science:

  • Computational Intelligence
  • Bioacoustics
  • Machine Learning

Background:

  • Marine mammal recognition and classification using Artificial Neural Networks (ANNs) present high-dimensional challenges.
  • The Chimp Optimization Algorithm (ChOA), a nature-inspired metaheuristic, shows promise but requires optimization for complex tasks.
  • Evolving ANNs with metaheuristic algorithms often involves high complexity and extended processing times.

Purpose of the Study:

  • To improve the Chimp Optimization Algorithm (ChOA) for training Artificial Neural Networks (ANNs) in marine mammal recognition.
  • To introduce a fuzzy logic-based approach (Fuzzy-ChOA) to dynamically adjust ChOA's exploration-exploitation balance, reducing complexity and processing time.
  • To evaluate the effectiveness of the proposed Fuzzy-ChOA for marine mammal sound classification using ANNs.

Main Methods:

  • Collection and pre-processing of underwater marine mammal sounds to create an experimental dataset.
  • Feature extraction from audio data, followed by classification using an Artificial Neural Network (ANN).
  • Development of Fuzzy-ChOA by integrating fuzzy logic to control ChOA parameters, optimizing the balance between exploration and exploitation phases.

Main Results:

  • The proposed Fuzzy-ChOA effectively tunes the exploration-exploitation phases of the optimization algorithm.
  • Comparative analysis demonstrated superior convergence speed and local optimal avoidance ability compared to benchmark algorithms.
  • The Fuzzy-ChOA-trained ANN achieved a higher classification rate and improved Receiver Operating Characteristics (ROC) for marine mammal recognition.

Conclusions:

  • Fuzzy logic integration enhances the Chimp Optimization Algorithm's adaptability and efficiency for complex classification tasks.
  • The Fuzzy-ChOA provides a more effective and computationally efficient method for training ANNs in marine mammal sound recognition.
  • The developed approach offers a significant advancement in automated marine mammal monitoring and identification systems.