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Applying genetic algorithm to LQR controller design, leveraging its global search capability to optimize weighting matrices using active suspension performance metrics as objective functions, thereby improving LQR design efficiency and performance. Implementation involves chromosome encoding for matrix parameters, fitness evaluation based on system response, and iterative optimization through selection, crossover, and mutation operations.

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The Gravitational Search Algorithm (GSA) is a novel heuristic optimization algorithm proposed by Esmat Rashedi and colleagues at Kerman University, Iran, in 2009. Inspired by the simulation of gravitational forces in physics, it belongs to the category of swarm intelligence optimization algorithms. GSA operates by treating search particles as celestial bodies moving through space, where gravitational interactions guide their motion according to dynamical laws. Particles with higher fitness values possess greater inertial mass, and gravitational forces drive all particles toward the heaviest mass, thus gradually converging to the optimal solution. In implementation, the algorithm calculates masses based on fitness values, updates velocities using gravitational forces, and iteratively refines particle positions. GSA demonstrates strong global search capabilities and rapid convergence for complex optimization problems.

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