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中文摘要：
针对传统区间优化算法求解高维问题耗时的缺点,本文将区间算法和遗传算法进行融合,给出了一种区间-遗传算法,该算法保留了传统区间优化算法简单、对问题本身信息要求不高的优点.重要的是在每次迭代中区间算法为遗传算法的搜索提供可靠区域,同时遗传算法为区间算法的区间分裂提供了一个方向、为区间删除给出了问题全局最优解的一个上界.最后给出了算法的收敛性证明,数值实验表明该算法相比传统区间优化算法有较高执行效率.
英文摘要：
To overcome the shortcoming of high computational cost of traditional interval optimization algorithms for high dimensional problems, an interval-genetic algorithm is presented that combines interval arithmetic and genetic algorithm. The algorithm has the advantages of simplicity and less knowledge about problems as traditional interval optimization algorithms. What is more, at each iteration the interval arithmetic provides the domains for the genetic algorithm to search, moreover, the genetic algorithm gives a direction to divide the reliable interval,and an upper bound of global optimum for a problem used to discard the intervals.Finally, a convergence is proved and numerical experiments show that the algorithm is more efficient than traditional interval optimization algorithms.
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