An Information Fashion Algorithm Based on Extremal Optimization

A hybrid Information Fashion Algorithm (IFA) based on Extremal Optimization (EOIFA) with adaptive levy mutation was proposed in this paper. It applied the idea of combination mechanism of global and local search. In the process of the global search, IFA is an evolutionary algorithm based on the simulation of simplified social models and the swarming theory that can quickly approach a approximate optimal solution. During the local search, as a powerful local search capabilities algorithm EO with adaptive leacutevy mutation helps IFA out of local optimal points. Simulation study and its application have proved its capability of strong global search and high immunity against premature convergence. Then EOIFA is applied to optimize PID controller parameters . The obtained results indicate that the new method proposed by this paper is feasible and effective.