Particle Swarm Optimization Algorithm for Finding Min-Max Values of 16 Benchmark Functions
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Resource Overview
This package implements a particle swarm optimization algorithm to find minimum and maximum values of 16 classical test functions, featuring an intuitive user interface with real-time dynamic 2D visualization that demonstrates how particle swarms navigate the search space to converge toward optimal solutions
Detailed Documentation
This package employs particle swarm optimization (PSO) to solve for minimum and maximum values across 16 benchmark functions. The implementation includes a user-friendly GUI interface that displays real-time dynamic 2D visualizations, illustrating how particle swarms collectively navigate through solution spaces to locate optimal values. The algorithm features velocity and position update mechanisms using social and cognitive components with inertia weights. The package provides comprehensive documentation with code examples demonstrating key functions like initialization, fitness evaluation, and particle update procedures, helping users understand PSO implementation and parameter tuning strategies for different optimization scenarios.
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