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中文摘要：
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（M^3）下降为0（max（l，logM）M^2）。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。
英文摘要：
In the process of Hardware/software co-design of spacecraft control computers, the multi-objective optimization is a key problem. The current strength Pareto evoIutionary algorithm has some advantages in solving high-dimensional multi-objective optimization problems, but the computing time complexity during the step of environmental selection is still very large. Aiming at this point, an improved algorithm was proposed. With the finite K-nearest neighbor method, new algorithm reduces the number of comparisons to lower the time-complexity from O（Ma） down to O（max（l, logM）M2）. The experimental results show that the proposed algorithm not only improves the running speed, but also acquires better convergence and distribution diversity than the original one.
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Proving total correctness and generating preconditions for loop programs via symbolic-numeric comput
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