Collaborative Optimization Using Hybrid Simulated Annealing Optimization and Sequential Quadratic Programming

Multidiscipline design optimization provides a promising methodology for large-scale system design and becomes an active field of optimization research. Facing the shortcomings of traditional collaborative optimization, such as time-consuming, being sensitive to the initial points, not converging, a new collaborate optimization by mixing simulated annealing and sequential quadratic programming (SA-SQP-CO) is presented. Firstly, the gradient-based method in system-level is replaced with the hybrid optimization strategy of the simulated annealing algorithm and sequential quadratic programming. Secondly, dynamic relaxation factors are used during the optimization process. The proposed SA-SQP-CO is compared to traditional collaborative optimization algorithm using two demonstrated examples. The first one is the design of gearbox and the second one is the design of continuous fiber-reinforced composite cantilever beam. The results show that the optimum obtained using the proposed approach is more superior and robust.

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