Murali Krishna Madugula, Santosh Kumar Majhi, Nibedan Panda
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引用次数: 2
摘要
子集和问题(Subset-Sum Problem,SSP)在各种实际应用中发挥着重要作用,其中包括密码学和编码理论,因为它在一些公钥密码学系统的功能中具有重要作用。考虑由 n 个实数组成的集合 S,其中有 2n - 1 个不同的子集,但不包括空集。SSP 的定义是确定 N 个子集,其中子集中元素的和必须是所有可能子集中最小的 N 个。这个问题在运筹学研究和实践中有多种应用。但是,这个问题的计算非常复杂。因此,本文旨在使用一种名为算术优化算法(AOA)的功能强大的元启发式算法来解决 SSP 问题。本文开发了一种新颖的优化算法,以减少目标之间的误差并获得一个解决方案,同时解决 SSA 问题。最后,仿真分析表明,建议的 AOA 在使用基准数据时能确保获得最佳结果。
An Efficient Arithmetic Optimization Algorithm for Solving Subset-sum Problem
The Subset-Sum Problem (SSP) ensures a significant role in various practical applications, which include cryptography and coding theory owing to the importance in the functionality of some of the public key cryptography systems. Consider the set S of n real numbers, where the 2n - 1 diverse subsets are presented without including the empty set. The SSP is defined as the determination of N subsets, where the summation of elements in the subset needs to be N the smallest over all the possible subsets. This problem was involved in diverse applications in operations research and practice. But, the problem is very complex in computation. Hence, this paper aims to solve the SSP with a well-enabled meta-heuristic algorithm named Arithmetic Optimization Algorithm (AOA). Here, a novel optimization algorithm is developed for reducing the error among the target and attained a solution, and also to solve the SSA issue. At last, the simulation analysis reveals that the suggested AOA can ensure optimal results when using the benchmark data.