基于遗传算法的协同优化方法

A New Collaborative Optimization Based on Genetic Algorithm

针对协同优化的系统级优化可行域可能不存在的问题,采用遗传算法,并借鉴逐步增强约束强度的思想,提出了基于遗传算法的协同优化算法.该算法利用各子学科返回的优化值,计算种群中个体的不可行度,根据不可行度和阈值来判定该个体是否为可行解.提出利用循环迭代次数调整阈值的方法,保证了系统级优化向一致性等式约束不满足度减小的方向进行,达到了有效增强子学科间一致性的目的.最后,利用减速器典型算例对该方法进行了验证,结果表明该方法的优化性能良好.

In view of that the feasible region is possibly inexistent during the collaborative optimization at system level, the genetic algorithm （GA） is introduced in combination with strengthening the constraint conditions step by step to develop a new GA-based collaborative optimization algorithm. In this algorithm the individual in feasibility in population is computed with the optimal values resulting from subsystems, then whether the solution to an individual is feasible depends on the infeasibility and its threshold. A method to adjust the threshold is proposed via cyclic iteration steps to ensure that the optimization at system level will go towards decreasing the unsatisfiability of constraint on consistency equality, thus achieving the goal to enhance the interdisciplinary consistency. Taking the design of a speed reducer as example, the performance of the optimization algorithm is proved excellent.