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What is Swarm Intelligence?

Swarm Intelligence is a fascinating field inspired by the collective behavior of social insects like ants, bees, and termites. It applies principles from nature to solve complex problems through decentralized, self-organized systems. In the realm of artificial intelligence assignment help, understanding these principles is crucial for students tackling assignments in this domain.

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Particle Swarm Optimization for Sphere Function

import numpy as np # Objective function (Sphere function as example) def sphere_function(x): return np.sum(x**2) # PSO function def particle_swarm_optimization(cost_function, dimensions, swarm_size=50, max_iterations=100): # Initialize swarm swarm_position = np.random.uniform(-5.0, 5.0, size=(swarm_size, dimensions)) swarm_velocity = np.random.uniform(-1.0, 1.0, size=(swarm_size, dimensions)) personal_best_position = swarm_position.copy() personal_best_cost = np.full(swarm_size, np.inf) global_best_cost = np.inf global_best_position = np.zeros(dimensions) # PSO loop for _ in range(max_iterations): for i in range(swarm_size): cost = cost_function(swarm_position[i]) if cost < personal_best_cost[i]: personal_best_cost[i] = cost personal_best_position[i] = swarm_position[i] if cost < global_best_cost: global_best_cost = cost global_best_position = swarm_position[i] # Update velocities and positions for i in range(swarm_size): swarm_velocity[i] = 0.5 * swarm_velocity[i] + \ 1.5 * np.random.rand(dimensions) * (personal_best_position[i] - swarm_position[i]) + \ 2.0 * np.random.rand(dimensions) * (global_best_position - swarm_position[i]) swarm_position[i] += swarm_velocity[i] return global_best_position, global_best_cost # Example usage best_position, best_cost = particle_swarm_optimization(sphere_function, dimensions=2) print(f"Best position found: {best_position}") print(f"Best cost found: {best_cost}")

In this example:

• sphere_function: The objective function (Sphere function).
• particle_swarm_optimization: Implements PSO to minimize the sphere function.

It initializes particle positions and velocities, updates them based on their personal best and global best, and iteratively refines them to find the optimal solution.

This code provides a basic understanding of PSO, suitable for introductory learning in Swarm Intelligence.

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