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A Feature Selection Based on Improved Artificial Hummingbird Algorithm Using Random Opposition-Based Learning for Solving Waste Classification Problem

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  • Mona A. S. Ali

    (Computer Science Department, College of Computer Science and Information Technology, King Faisal University, Al Ahsa 400, Saudi Arabia
    Faculty of Computers and Artificial Intelligence, Benha University, Benha 12311, Egypt
    These authors contributed equally to this work.)

  • Fathimathul Rajeena P. P.

    (Computer Science Department, College of Computer Science and Information Technology, King Faisal University, Al Ahsa 400, Saudi Arabia
    These authors contributed equally to this work.)

  • Diaa Salama Abd Elminaam

    (Faculty of Computers and Artificial Intelligence, Benha University, Benha 12311, Egypt
    Computer Science Department, Faculty of Computer Science, Misr International University, Cairo 12585, Egypt
    Faculty of Information Technology, Middle East University, Amman 11831, Jordan
    These authors contributed equally to this work.)

Abstract

Recycling tasks are the most effective method for reducing waste generation, protecting the environment, and boosting the overall national economy. The productivity and effectiveness of the recycling process are strongly dependent on the cleanliness and precision of processed primary sources. However, recycling operations are often labor intensive, and computer vision and deep learning (DL) techniques aid in automatically detecting and classifying trash types during recycling chores. Due to the dimensional challenge posed by pre-trained CNN networks, the scientific community has developed numerous techniques inspired by biology, swarm intelligence theory, physics, and mathematical rules. This research applies a new meta-heuristic algorithm called the artificial hummingbird algorithm (AHA) to solving the waste classification problem based on feature selection. However, the performance of the AHA is barely satisfactory; it may be stuck in optimal local regions or have a slow convergence. To overcome these limitations, this paper develops two improved versions of the AHA called the AHA-ROBL and the AHA-OBL. These two versions enhance the exploitation stage by using random opposition-based learning (ROBL) and opposition-based learning (OBL) to prevent local optima and accelerate the convergence. The main purpose of this paper is to apply the AHA-ROBL and AHA-OBL to select the relevant deep features provided by two pre-trained models of CNN (VGG19 & ResNet20) to recognize a waste classification. The TrashNet dataset is used to verify the performance of the two proposed approaches (the AHA-ROBL and AHA-OBL). The effectiveness of the suggested methods (the AHA-ROBL and AHA-OBL) is compared with that of 12 modern and competitive optimizers, namely the artificial hummingbird algorithm (AHA), Harris hawks optimizer (HHO), Salp swarm algorithm (SSA), aquila optimizer (AO), Henry gas solubility optimizer (HGSO), particle swarm optimizer (PSO), grey wolf optimizer (GWO), Archimedes optimization algorithm (AOA), manta ray foraging optimizer (MRFO), sine cosine algorithm (SCA), marine predators algorithm (MPA), and rescue optimization algorithm (SAR). A fair evaluation of the proposed algorithms’ performance is achieved using the same dataset. The performance analysis of the two proposed algorithms is applied in terms of different measures. The experimental results confirm the two proposed algorithms’ superiority over other comparative algorithms. The AHA-ROBL and AHA-OBL produce the optimal number of selected features with the highest degree of precision.

Suggested Citation

  • Mona A. S. Ali & Fathimathul Rajeena P. P. & Diaa Salama Abd Elminaam, 2022. "A Feature Selection Based on Improved Artificial Hummingbird Algorithm Using Random Opposition-Based Learning for Solving Waste Classification Problem," Mathematics, MDPI, vol. 10(15), pages 1-34, July.
  • Handle: RePEc:gam:jmathe:v:10:y:2022:i:15:p:2675-:d:875313
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    Cited by:

    1. Walaa N. Ismail & Fathimathul Rajeena P. P. & Mona A. S. Ali, 2023. "A Meta-Heuristic Multi-Objective Optimization Method for Alzheimer’s Disease Detection Based on Multi-Modal Data," Mathematics, MDPI, vol. 11(4), pages 1-22, February.
    2. Adrian Marius Deaconu & Daniel Tudor Cotfas & Petru Adrian Cotfas, 2023. "Advanced Optimization Methods and Applications," Mathematics, MDPI, vol. 11(9), pages 1-7, May.

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