An improved cat swarm optimization algorithm and application research

Cat swarm optimization algoritiim(CSO) is easy to oscillation resulting low optimizing precision in the vicinity of the local or global extremum in later iterations, due to the fixed parameters, such as inertia weight, the step size factor. In this paper, cat swarm optimization algorithm based on adaptive inertia weight(AWCSO) is proposed to overcome the above problem. The weighting value will change with the cat position change by introducing the adaptive inertia weight. The result of test function optimization and weighted matrix tuning of linear quadratic regulator(LQR) optimal control by MATLAB simulation shows that the optimization ability of the improved algorithm is better than particle swarm optimization algorithm(PSO) and basic CSO.