Hybrid Genetic Algorithm-PSO Implementation Code
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Resource Overview
Implementation of Hybrid Genetic Algorithm and Particle Swarm Optimization Algorithm
Detailed Documentation
This code provides a comprehensive implementation combining Genetic Algorithm (GA) and Particle Swarm Optimization (PSO) methodologies. The hybrid approach leverages GA's crossover and mutation operators for global exploration while utilizing PSO's velocity and position update mechanisms for local refinement. Key features include population initialization with random solutions, fitness evaluation functions, tournament selection for parent selection, uniform crossover for offspring generation, Gaussian mutation for diversity maintenance, and inertia weight adaptation for PSO convergence control. The implementation includes termination criteria based on maximum generations or fitness threshold achievement, with detailed comments explaining each algorithmic component and parameter tuning recommendations for different optimization scenarios.
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