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Implementation of Honey Bee Algorithm for Optimization (Python), Summaries of Optimization Techniques in Engineering

SNDT Women's UniversityOptimization Techniques in Engineering

This document explains the Honey Bee Algorithm, a nature-inspired optimization technique based on honey bee foraging behavior. It includes the theory, algorithm steps, mathematical model, Python code implementation, output analysis, observations, and results for solving an optimization problem. Ideal for students studying Artificial Intelligence, Soft Computing, and Optimization Techniques.

Typology: Summaries

2025/2026

Uploaded on 06/16/2026

ai1-sai-dhatri-reddy
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Experiment No: 8
Title
Implementation of Honey Bee Algorithm for Optimization
Aim
To implement the Honey Bee Algorithm to find optimal solution of a problem.
Theory
Honey Bees Algorithm is a nature-inspired optimization algorithm based on the foraging
behavior of honey bees. It mimics how bees search for food and share information about
good food sources.
Key principles:
Bees search randomly for food sources (solutions).
Best bees are selected based on fitness.
More bees are recruited around good solutions (local search).
Remaining bees explore new areas (global search).
Algorithm balances exploration and exploitation.
Mathematical Model
Objective function: f(x) = -(x^2) + 5
Goal: Maximize the function
Objectives
1. To understand Bees Algorithm.
2. To apply it to an optimization problem.
3. To observe convergence behavior.
Problem Statement
Find value of ( x ) that maximizes: f(x) = -(x^2) + 5
Bees Algorithm Steps
1. Initialize bees randomly (solutions).
2. Evaluate fitness of each bee.
3. Select best bees (elite sites).
4. Perform neighborhood (local) search around best bees.
5. Recruit more bees near good solutions.
6. Perform global random search for remaining bees.
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Experiment No: 8

Title

Implementation of Honey Bee Algorithm for Optimization

Aim

To implement the Honey Bee Algorithm to find optimal solution of a problem.

Theory

Honey Bees Algorithm is a nature-inspired optimization algorithm based on the foraging behavior of honey bees. It mimics how bees search for food and share information about good food sources. Key principles:  Bees search randomly for food sources (solutions).  Best bees are selected based on fitness.  More bees are recruited around good solutions (local search).  Remaining bees explore new areas (global search).  Algorithm balances exploration and exploitation.

Mathematical Model

Objective function: f(x) = - (x^2) + 5 Goal: Maximize the function

Objectives

  1. To understand Bees Algorithm.
  2. To apply it to an optimization problem.
  3. To observe convergence behavior.

Problem Statement

Find value of ( x ) that maximizes: f(x) = - (x^2) + 5

Bees Algorithm Steps

  1. Initialize bees randomly (solutions).
  2. Evaluate fitness of each bee.
  3. Select best bees (elite sites).
  4. Perform neighborhood (local) search around best bees.
  5. Recruit more bees near good solutions.
  6. Perform global random search for remaining bees.
  1. Repeat for given iterations.
  2. Output best solution.

Python Code

import random

Fitness function

def fitness(x): return - (x**2) + 5 # maximum at x = 0

Initialize bees (solutions)

def initialize_bees(n): return [random.uniform(-5, 5) for _ in range(n)]

Neighborhood search

def local_search(x): return x + random.uniform(-0.5, 0.5)

Bees Algorithm

def bees_algorithm(n=10, iterations=20): bees = initialize_bees(n) for i in range(iterations):

Evaluate fitness

bees = sorted(bees, key=fitness, reverse=True)

Select best 3 bees

best_bees = bees[:3] new_bees = []

Local search around best bees

for b in best_bees: for _ in range(3): # recruit bees new_bees.append(local_search(b))

Global random search

while len(new_bees) < n: new_bees.append(random.uniform(-5, 5)) bees = new_bees

Print best solution

best = max(bees, key=fitness) print(f"Iteration {i+1}: Best x = {best:.4f}, Fitness = {fitness(best):.4f}") return max(bees, key=fitness)

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