基于车辆协同的遗传规划超启发式求解不确定有能力电弧路径问题

IF 4.6 2区 计算机科学 Q2 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE
Jordan MacLachlan;Yi Mei;Juergen Branke;Mengjie Zhang
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引用次数: 34

摘要

由于其与灾后操作、抄表和民用垃圾收集直接相关,不确定电容电弧路由问题(UCRP)是一个重要的优化问题。随机模型对研究至关重要,因为它们比确定性模型更准确地代表了现实世界。尽管在解决不确定性下的路线问题方面进行了广泛的研究,但很少有人考虑过UCRP,也没有人考虑车辆之间的协作来处理不确定性的负面影响。本文提出了一种新的解决方案构建程序(SCP),该程序在协作的多车辆框架内生成UCRP的解决方案。它由两种类型的协作活动组成:一种是当车辆意外消耗容量(路线故障)时,另一种是在加注过程中。然后,我们提出了一种遗传规划超启发式(GPHH)算法来进化协作框架中使用的路由策略。实验研究表明,在常用的测试问题上,具有车辆协作和GP进化路由策略的新启发式算法显著优于现有算法。这在任务和车辆数量较多的情况下尤其如此。这清楚地表明了车辆协作在处理不确定环境方面的优势,以及新提出的算法的有效性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Genetic Programming Hyper-Heuristics with Vehicle Collaboration for Uncertain Capacitated Arc Routing Problems
Due to its direct relevance to post-disaster operations, meter reading and civil refuse collection, the Uncertain Capacitated Arc Routing Problem (UCARP) is an important optimisation problem. Stochastic models are critical to study as they more accurately represent the real world than their deterministic counterparts. Although there have been extensive studies in solving routing problems under uncertainty, very few have considered UCARP, and none consider collaboration between vehicles to handle the negative effects of uncertainty. This article proposes a novel Solution Construction Procedure (SCP) that generates solutions to UCARP within a collaborative, multi-vehicle framework. It consists of two types of collaborative activities: one when a vehicle unexpectedly expends capacity (route failure), and the other during the refill process. Then, we propose a Genetic Programming Hyper-Heuristic (GPHH) algorithm to evolve the routing policy used within the collaborative framework. The experimental studies show that the new heuristic with vehicle collaboration and GP-evolved routing policy significantly outperforms the compared state-of-the-art algorithms on commonly studied test problems. This is shown to be especially true on instances with larger numbers of tasks and vehicles. This clearly shows the advantage of vehicle collaboration in handling the uncertain environment, and the effectiveness of the newly proposed algorithm.
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来源期刊
Evolutionary Computation
Evolutionary Computation 工程技术-计算机:理论方法
CiteScore
6.40
自引率
1.50%
发文量
20
审稿时长
3 months
期刊介绍: Evolutionary Computation is a leading journal in its field. It provides an international forum for facilitating and enhancing the exchange of information among researchers involved in both the theoretical and practical aspects of computational systems drawing their inspiration from nature, with particular emphasis on evolutionary models of computation such as genetic algorithms, evolutionary strategies, classifier systems, evolutionary programming, and genetic programming. It welcomes articles from related fields such as swarm intelligence (e.g. Ant Colony Optimization and Particle Swarm Optimization), and other nature-inspired computation paradigms (e.g. Artificial Immune Systems). As well as publishing articles describing theoretical and/or experimental work, the journal also welcomes application-focused papers describing breakthrough results in an application domain or methodological papers where the specificities of the real-world problem led to significant algorithmic improvements that could possibly be generalized to other areas.
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