Hybrid Ant Colony Optimization Algorithm for Multiple Knapsack Problem

S. Fidanova
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引用次数: 4

Abstract

0pt0pt Multiple Knapsack Problem (MKP) is a difficult combinatorial optimization problem. It is one of the most studied optimization problem, because a lot of economical, practical and industrial problems can be described as knapsack problem. MKP is a NP-hard problem and requires the use of a large amount of computer resources if traditional numerical method is applied. Therefore methaeuristic methods are more suitable for such complex problems. We apply Ant Colony Optimization (ACO), because it is one of the best metaheuristic methods, prepared for solving combinatorial optimization problems. it is an approximate method, that finds close to optimal solutions. In this paper we propose local search procedure, which we combine with a main ACO algorithm. The aim is improvement of the algorithm performance and achievement of better solutions.
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多背包问题的混合蚁群优化算法
多背包问题(Multiple backpack Problem, MKP)是一个复杂的组合优化问题。由于许多经济的、实用的和工业的问题都可以被描述为背包问题,它是研究最多的优化问题之一。MKP是一个NP-hard问题,如果采用传统的数值方法,需要使用大量的计算机资源。因此,方法更适合于此类复杂的问题。我们采用蚁群优化算法,因为它是解决组合优化问题的最好的元启发式方法之一。它是一种近似方法,可以找到接近最优解。本文提出了一种局部搜索算法,并将其与一种主要的蚁群算法相结合。其目的是提高算法性能并获得更好的解。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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