322 - PSO Using steel optimization

Опубликовано: 18 Январь 2025
на канале: DigitalSreeni

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Code link: https://github.com/bnsreenu/python_fo...

Finding the best alloy with maximum yield strength using Particle Swarm Optimization

In this example, we will work with the steel alloy data set.
Download from here: https://www.kaggle.com/datasets/fuarr...

The data set contains the elemental composition of different alloys and their respective yield and tensile strengths. A machine learning model can be trained on this data, allowing us to predict the strength of an alloy based on its chemical composition. But, for this exercise, let us try to find the optimized alloy composition with the best yield strength.

Let us explore PSO:

Particle Swarm Optimization

PSO is a swarm intelligence algorithm that is inspired by the behavior of social organisms such as flocks of birds or schools of fish.

The algorithm creates a population of particles, each representing a candidate solution, that move through the search space based on their individual velocity and the collective influence of the best solutions found by the swarm.

The algorithm updates the particles' positions and velocities based on the fitness of the current solution and the local and global best solutions found so far. It aims to balance exploration and exploitation by encouraging particles to explore new regions of the search space while also following promising solutions.

PSO is suitable for solving nonlinear and dynamic optimization problems, such as in control systems, machine learning, and signal processing.

PSO has been used for feature selection, image segmentation, and classification in microscopy images. For example, it has been used to optimize the parameters of texture descriptors for image segmentation, and to select the most discriminative features for cell classification.