Genetic Algorithm and Particle Swarm Optimization Hybrid Algorithm
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Resource Overview
This MATLAB program implements a hybrid optimization approach combining Genetic Algorithm (GA) and Particle Swarm Optimization (PSO), featuring significantly improved optimization efficiency and robust avoidance of local optima through integrated evolutionary and swarm intelligence mechanisms.
Detailed Documentation
This algorithm is implemented through a MATLAB program that integrates Genetic Algorithm and Particle Swarm Optimization techniques. The hybrid approach leverages GA's chromosome crossover and mutation operations alongside PSO's velocity-position update mechanism, creating a synergistic optimization framework. Through this integration, the optimization efficiency is substantially enhanced, effectively preventing the solution from becoming trapped in local optima. The implementation includes key functions for population initialization, fitness evaluation, and hybrid operator selection, maintaining a balanced exploration-exploitation tradeoff throughout the optimization process.
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