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首页> 外文期刊>Arabian Journal for Science and Engineering. Section A, Sciences >An Adaptive Spiking Neural P System for Solving Vehicle Routing Problems
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An Adaptive Spiking Neural P System for Solving Vehicle Routing Problems

机译：求解车辆路径问题的自适应尖峰神经P系统

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摘要

The capabilities of membrane computing frameworks in solving multi-objective constrained optimization problems haveinvited many researchers to focus their efforts on developing new methods and computational paradigms. Getting motivatedfrom the computational completeness of membrane computing systems (P systems), this paper proposes a new way of solvingvehicle routing problems (VRP) using one of the most eminent membrane computing frameworks called spiking neuralP systems (SNPS). A new model for SNPS has been recommended both for finding the optimal solutions and for optimizingthe parameters that are used in the calculation of minimum feasible insertion cost of the customer insertion phase of VRPwithout using any heuristic operators. The SNPS suggested here is an adaptive SNPS in which some potentials (ATSNPS)with learning and training facilities are incorporated. Being an NP-hard problem with numerous applications in many areassuch as gas distribution management, postal delivery, and truck dispatching, the benefits of this study are far-reaching. Here,a variant of VRP called VRP with time windows (VRPTW) has been used in the proposed system. Since this is the firstattempt to find solutions of VRP using ATSNPS, a comparison has been made with the algorithms used over VRPTW. Theanalysis of results proved that the proposed ATSNPS is substantially superior to the state-of-the-art algorithms in terms ofcomputational time and optimizing the attributes such as the average number of vehicles used and the total distance traveled.
机译：膜计算框架解决多目标约束优化问题的能力吸引了许多研究人员将精力集中在开发新方法和计算范例上。从膜计算系统（P系统）的计算完整性出发，本文提出了一种使用最著名的膜计算框架（称为尖峰神经P系统（SNPS））来解决车辆路径问题（VRP）的新方法。已建议使用新的SNPS模型，以寻找最佳解决方案并优化用于计算VRP客户插入阶段的最小可行插入成本的参数，而无需使用任何启发式运算符。这里建议的SNPS是一种自适应SNPS，其中结合了具有学习和培训功能的某些潜力（ATSNPS）。作为NP难题，在气体分配管理，邮政运输和卡车调度等许多领域都有大量应用，这项研究的好处是深远的。在此，在建议的系统中使用了VRP的一种变体，称为带有时间窗口的VRP（VRPTW）。由于这是首次尝试使用ATSNPS查找VRP解决方案，因此已与VRPTW上使用的算法进行了比较。结果分析证明，所提出的ATSNPS在计算时间和优化属性（例如平均使用车辆数和总行驶距离）方面明显优于最新算法。

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