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在航天器控制计算机的软硬件协同设计过程中,需要解决多目标优化问题。当前的强度帕累托进化算法在求解高维多目标优化问题时具有优势,但是在环境选择阶段的计算时间复杂度仍然较大。文章针对这一问题,提出了一种改进算法。新的算法采用有限K近邻方法,减少了原算法中K近邻策略的比较次数,使时间复杂度由O(M3)下降为O(max(l,log M)M2)。试验结果表明文中算法的计算速度更快,并且具有更优的收敛性和分布多样性特征。
In the spacecraft control computer hardware and software co-design process, need to solve the multi-objective optimization problem. The current strength Pareto evolutionary algorithm has advantages in solving high-dimensional multi-objective optimization problems, but the computation time complexity in the environment selection phase is still large. In order to solve this problem, this paper proposes an improved algorithm. The new algorithm uses the finite K-nearest neighbor method to reduce the number of times that the K-nearest neighbor strategy is compared in the original algorithm, reducing the time complexity from O (M3) to O (max (l, log M) M2). Experimental results show that the proposed algorithm is faster in computation speed and has better convergence and distribution diversity.