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Genetic Algorithm (GA) is a stochastic optimization search method inspired by biological evolution principles (survival of the fittest, natural selection mechanism). Its main characteristics include operating directly on structural objects without requiring derivative calculations or function continuity constraints; possessing inherent implicit parallelism and superior global optimization capabilities; employing probabilistic optimization methods that automatically acquire and guide the search space while adaptively adjusting search directions without deterministic rules. For fitness function optimization, genetic algorithms achieve faster convergence, reasonable optimization results, and good robustness. Genetic algorithms operate on parameter encodings rather than parameters themselves and utilize multiple search points simultaneously.

MATLAB 221 views Tagged

Quantum Particle Swarm Optimization (QPSO) is a population-based probabilistic algorithm that addresses the limitation of traditional Particle Swarm Optimization where particle velocity constraints restrict search space exploration to confined regions. This implementation in MATLAB demonstrates how quantum mechanics concepts enable global optimization through position updates without velocity parameters.

MATLAB 253 views Tagged