Introducing Bee-Eater Hunting Strategy Algorithm for IoT-Based Green House Monitoring and Analysis

Bee-Eater Hunting Algorithm (BEH)

Link to the paper:

• https://ieeexplore.ieee.org/abstract/document/9953726
• DOI: 10.1109/SCIoT56583.2022.9953726

Please cite below:

• Mousavi, Seyed Muhammad Hossein. "Introducing bee-eater hunting strategy algorithm for IoT-based green house monitoring and analysis." 2022 Sixth International Conference on Smart Cities, Internet of Things and Applications (SCIoT). IEEE, 2022.

Fuzzy Bee-Eater Hunting Algorithm: A Novel Optimization Approach

Overview

This repository contains the implementation of the Fuzzy Bee-Eater Hunting Algorithm (Fuzzy BEH), as presented in the paper. The Fuzzy BEH algorithm is a novel optimization technique inspired by the hunting behavior of bee-eater birds. It integrates fuzzy logic into the core of the Bee-Eater Hunting Algorithm (BEH) for enhanced performance in clustering, regression, and general optimization tasks.

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Table of Contents

• Introduction
• Algorithm Overview
• Applications
  • Clustering
  • Regression
• Performance Metrics

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Introduction

The Fuzzy Bee-Eater Hunting Algorithm (Fuzzy BEH) is an evolutionary optimization algorithm that combines:

1. The natural hunting strategy of bee-eater birds.
2. Fuzzy logic to handle uncertainties and improve decision-making during optimization.

The algorithm is capable of solving a wide range of optimization problems, including clustering, regression, and classification tasks, making it versatile for real-world applications.

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Algorithm Overview

The core principles of the Bee-Eater Hunting Algorithm are:

• Peak Power (( \zeta )): Guides the movement of the solutions toward better regions of the search space.
• Adjustment Power (( \eta )): Fine-tunes the movement using pitch, yaw, and roll mechanisms.
• Fuzzy Logic Integration:
  • Adds membership functions for dynamic adjustment of algorithm parameters.
  • Handles the trade-off between exploration and exploitation more effectively.

The Fuzzy BEH algorithm is structured as:

1. Initialization:
   • Randomly generate a population of solutions.
   • Assign initial costs and memberships.
2. Iterative Optimization:
   • Evaluate and rank solutions.
   • Update positions based on peak power, adjustment power, and fuzzy rules.
   • Apply mutation for diversity.
3. Convergence:
   • Stop when the maximum number of iterations is reached or when the solution converges.

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Applications

Clustering

The Fuzzy BEH algorithm is applied for clustering tasks by:

• Optimizing cluster centers in a multi-dimensional space.
• Using fuzzy memberships to assign data points to clusters.

Regression

The Fuzzy BEH algorithm is applied for regression tasks by:

• Optimizing polynomial coefficients for predictive models.
• Handling noisy and non-linear relationships between input features and target variables.

Performance Metrics

For evaluating the performance of Fuzzy BEH, the following metrics are used:

• Clustering:
  • Silhouette Score: Measures the quality of clustering results.
  • Intra-cluster Distance: Quantifies the compactness of clusters.
• Regression:
  • Mean Squared Error (MSE): Measures prediction error.
  • R² Score: Indicates the proportion of variance explained by the model.