一种基于贪心蚁群优化的两阶段算法求解行窃问题

IF 4.6 2区 计算机科学 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE
Zheng Zhang, Xiao-Yun Xia, Zi-Jia Wang, You-Zhen Jin, Wei-Zhi Liao, Jun Zhang
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引用次数: 0

摘要

旅行小偷问题(TTP)结合了旅行推销员问题(TSP)和背包问题(KP)这两个 NP 难问题,其求解更为复杂。在 TTP 中,推销员需要选择行进路线,同时选择物品,以获得最大利润。因此,TTP 的解决主要包括两个阶段,即路线规划和物品选择。在路线规划的第一阶段,传统算法只考虑距离最小化,而忽略了物品的属性。针对这一问题,提出了一种物品分类算法(ICA),根据物品的利润和分布模式,提前计算出物品的优化组合。此外,还提出了一种贪婪蚁群优化算法(GACO)来规划旅行路线,将所选物品所在的城市放在旅行路线的后一段,从而优化销售员的利润。因此,GACO 可以生成一条有利于第二阶段商品选择方案优化的旅行路线。实验结果表明,与传统的林-柯尼希恩(LK)启发式规则相比,GACO 更有利于优化 TTP。在第二阶段的项目选择方面,传统算法的项目选择策略需要设计复杂的规则,而且通常只对项目进行局部搜索,这使得算法容易陷入局部最优。本文在遗传算法(GA)的基础上设计了一种遗传项搜索策略(GISS),GISS 不需要设计复杂的规则,可以对项进行有效的全局搜索。实验结果也显示了 GISS 在 TTP 项目选择方案中的有效性,其性能优于其他最先进的算法。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
A two-stage algorithm based on greedy ant colony optimization for travelling thief problem

The travelling thief problem (TTP) combines two NP-hard problems, traveling salesman problem (TSP) and knapsack problem (KP), which is more complicated for solving. In TTP, the salesman needs to choose the travel route and select the items at the same time to maximize the profit. Consequently, the resolution of TTP essentially encompasses two stages, including route planning and item selection. In the first-stage of route planning, conventional algorithms solely on distance minimization and ignore the attributes of items. To address this issue, an item classification algorithm (ICA) is proposed to compute an optimized combination of items in advance based on their profits and distribution patterns. Furthermore, a greedy ant colony optimization (GACO) is proposed to plan travel route which places the city, where the selected item located, at the later segment of the travel route, optimizing the salesman’s profit. Hence, GACO can produce a travel route that facilitates the optimization in the second-stage item selection scheme. Experimental results illustrate that GACO is more facilitative in optimizing TTP compared to the route obtains by traditional Lin–Kernighan (LK) heuristic rules. With regard to item selection in the second-stage, the traditional algorithm’s item selection strategy requires the design of complex rules, and usually only local search is performed on items, which makes the algorithm prone to falling into local optima. This paper designs a genetic item search strategy (GISS) based on genetic algorithm (GA), GISS does not require the design of complex rules and can perform an effective global search on items. The experimental results also show the efficacy of GISS in item selection schemes for TTP, which can outperform other state-of-the-art algorithms.

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来源期刊
Complex & Intelligent Systems
Complex & Intelligent Systems COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE-
CiteScore
9.60
自引率
10.30%
发文量
297
期刊介绍: Complex & Intelligent Systems aims to provide a forum for presenting and discussing novel approaches, tools and techniques meant for attaining a cross-fertilization between the broad fields of complex systems, computational simulation, and intelligent analytics and visualization. The transdisciplinary research that the journal focuses on will expand the boundaries of our understanding by investigating the principles and processes that underlie many of the most profound problems facing society today.
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