Adaptive collaborative optimization based on GASA algorithm

Aiming at the convergence difficulties and the low optimization efficiency of Collaborative Optimization (CO), self-adaptive concept and a hybrid optimization algorithm were proposed. An adaptive penalty function was constructed to convert system-level constraints so as to overcome the defects caused by the internal definition of collaborative optimization. With CO's characteristics, the hybrid optimization algorithm (Genetic Algorithm and Simulated Annealing, GASA) was proposed to enhance CO's efficiency at system level. GASA combined advantages of both genetic algorithm and simulated annealing. An example of landing gear was taken to verify the proposed optimizing method. The result showed that the method improved convergence and search efficiency with good optimization performance.