Immune clonal selection algorithm for capacitated arc routing problem

In existing metaheuristics for solving the capacitated arc routing problem, traversal local search operators are often used to explore neighbors of the current solutions. This mechanism is beneficial for finding high-quality solutions; however, it entails a large number of function evaluations, causing high computational complexity. Hence, there is a need to further enhance the efficiency of such algorithms. This paper proposes a high-efficiency immune clonal selection algorithm for capacitated arc routing instances within a limited number of function evaluations. First, an improved constructive heuristic is used to initialize the antibody population. The initial antibodies generated by this heuristic help accelerate the algorithm’s convergence. Second, we show how an immune clonal selection algorithm can select in favor of these high-quality antibodies. By adopting a variety of different strategies for different clones of the same antibody, it not only promotes cooperation and information exchanging among antibodies, but also increases diversity and speeds up convergence. Third, two different antibody repair operations are proposed for repairing various kinds of infeasible solutions. These operations cause infeasible solutions to move towards global optima. Experimental studies demonstrate improved performance over state-of-art algorithms, especially on medium-scale instances.     

基于免疫克隆选择算法的固定费用运输问题优化

固定费用运输问题（fcTP）是物流运输中的高级问题,属于NP难题,较难得到最优解。针对现有方法存在的不足,提出了基于免疫克隆选择的fcTP求解算法。在该算法中,抗体采用矩阵形式编码,初始抗体群随机产生,通过迭代进行克隆选择、克隆抑制、基因变异等操作,对潜在解空间进行寻优搜索。实验结果表明,对于fcTP的优化,免疫克隆选择算法能够快速收敛于全局最优解,克服了遗传算法收敛速度慢和容易陷入局部最优的缺点。     

基于多记忆抗体克隆选择的人工免疫网络算法

在优化克隆算法的研究中,针对传统的克隆选择算法存在收敛性差和局部最优问题,提出一种多记忆抗体克隆选择原理的人工免疫网络算法。在克隆选择算法的基础上通过引入替代阀值因子,利用随机生成的新抗体组成种群替代原种群中对抗原亲和力最小抗体,同时增设变异概率的概念,达到在一定程度上避免记忆抗体种群的退化现象,提高算法的全局优化能力,避免陷入局部最优。仿真结果表明,算法加快了种群亲和力成熟的进程,随着进化代数的增加检测率总体呈上升趋势,能更好的应用于大规模各种识别问题中。     

多媒体通信中组播路由选择的免疫算法

随着多媒体通信业务的迅速发展,为了高效地解决多媒体通信中的Qos组播路由问题,提出了一个新的组播路由免疫算法。该算法是根据人或者其它高等动物免疫系统的机理而设计的,将目标函数和一部分不等式约束条件作为抗原,将问题的解作为抗体,依据抗原与抗体之间的亲和力以及抗体之间的亲和力对解进行评价和选择,通过抗体之间的相互激励来提高最优点附近的搜索效率,通过记忆细胞对抗体的抑制作用有效地摆脱局部最优点。仿真试验表明,该算法不仅有效可行,而且能够迅速逃出局部最优解,并很快收敛到全局最优解。     

A multi-crossover and adaptive island based population algorithm for solving routing problems

We propose a multi-crossover and adaptive island based population algorithm （MAIPA）. This technique divides the entire population into subpopulations, or demes, each with a different crossover function, which can be switched according to the efficiency. In addition, MAIPA reverses the philosophy of conventional genetic algorithms. It gives priority to the autonomous improvement of the individuals （at the mutation phase）, and introduces dynamism in the crossover probability. Each subpopulation begins with a very low value of crossover probability, and then varies with the change of the current generation number and the search performance on recent generations. This mechanism helps prevent premature convergence. In this research, the effec- tiveness of this technique is tested using three well-known routing problems, i.e., the traveling salesman problem （TSP）, capaci- tated vehicle routing problem （CVRP）, and vehicle routing problem with backhauls （VRPB）. MAIPA proves to be better than a traditional island based genetic algorithm for all these three problems.     

克隆选择免疫遗传算法对高维0/1背包问题应用

针对遗传算法求解高维背包问题收敛速度慢、易于陷入局部最优的缺点,基于生物免疫系统克隆选择原理,提出一种克隆选择免疫遗传算法。该算法中抗体采用二进制编码,通过抗体浓度设计抗体亲和力,进化群分离为可行群和非可行群,进化过程仅可行抗体动态克隆和突变,非可行抗体经修复算子获可行抗体。数值实验中,选取三种著名的算法用于四种高维的背包问题求解,结果表明:所提算法较其他算法具有更强的约束处理能力和快速收敛的效果。     

An enhanced ant colony optimization (EACO) applied to capacitated vehicle routing problem

In this paper, an enhanced ant colony optimization (EACO) is proposed for capacitated vehicle routing problem. The capacitated vehicle routing problem is to service customers with known demands by a homogeneous fleet of fixed capacity vehicles starting from a depot. It plays a major role in the field of logistics and belongs to NP-hard problems. Therefore, it is difficult to solve the capacitated vehicle routing problem directly when solutions increase exponentially with the number of serviced customers. The framework of this paper is to develop an enhanced ant colony optimization for the capacitated vehicle routing problem. It takes the advantages of simulated annealing and ant colony optimization for solving the capacitated vehicle routing problem. In the proposed algorithm, simulated annealing provides a good initial solution for ant colony optimization. Furthermore, an information gain based ant colony optimization is used to ameliorate the search performance. Computational results show that the proposed algorithm is superior to original ant colony optimization and simulated annealing separately reported on fourteen small-scale instances and twenty large-scale instances.     

A deterministic tabu search algorithm for the capacitated arc routing problem

The capacitated arc routing problem (CARP) is a difficult optimisation problem in vehicle routing with applications where a service must be provided by a set of vehicles on specified roads. A heuristic algorithm based on tabu search is proposed and tested on various sets of benchmark instances. The computational results show that the proposed algorithm produces high quality results within a reasonable computing time. Some new best solutions are reported for a set of test problems used in the literature.     

基于变异记忆矩阵的克隆选择算法

利用免疫系统的免疫记忆机制,提出一种适于函数优化的基于变异记忆矩阵的克隆选择算法.首先,利用变异记忆矩阵保存进化中有用的变异信息,以引导抗体的克隆和变异操作,加强局部搜索能力;然后,利用当代种群的综合信息生成新抗体进入种群,以加强全局搜索能力;最后,对最优抗体进行自学习,以提高算法结果的精度.标准函数仿真表明,该算法适合求解复杂函数优化问题,具有收敛速度快、全局收敛能力强、精度高、鲁棒性强的优点.     

A variable neighborhood descent heuristic for arc routing problems with time-dependent service costs

This paper proposes a variable neighborhood descent heuristic for solving a capacitated arc routing problem with time-dependent service costs. The problem is motivated by winter gritting applications where the timing of each intervention is crucial. The variable neighborhood descent is based on neighborhood structures that manipulate arcs or sequences of arcs. Computational results are reported on problems derived from classical capacitated arc routing problem instances. A comparison is also provided with an alternative approach where the arc routing problem is solved after being transformed into an equivalent node routing problem.     

A variable neighborhood descent heuristic for arc routing problems with time-dependent service costs

This paper proposes a variable neighborhood descent heuristic for solving a capacitated arc routing problem with time-dependent service costs. The problem is motivated by winter gritting applications where the timing of each intervention is crucial. The variable neighborhood descent is based on neighborhood structures that manipulate arcs or sequences of arcs. Computational results are reported on problems derived from classical capacitated arc routing problem instances. A comparison is also provided with an alternative approach where the arc routing problem is solved after being transformed into an equivalent node routing problem.     

A two-phase metaheuristic for the cumulative capacitated vehicle routing problem

The cumulative capacitated vehicle routing problem, which aims to minimize the total arrival time at customers, is a relatively new variant of vehicle routing problem. It can be used to model many real-world applications, e.g., the important application arisen from the humanitarian aid after a natural disaster. In this paper, an approach, called two-phase metaheuristic, is proposed to deal with this problem. This algorithm starts from a solution. At each iteration, two interdependent phases use different perturbation and local search operators for solution improvement. The effectiveness of the proposed algorithm is empirically investigated. The comparison results show that the proposed algorithm is promising. Moreover, for nine benchmark instances, the two-phase metaheuristic can find better solutions than those reported in the previous literature.     

A genetic algorithm based approach to vehicle routing problem with simultaneous pick-up and deliveries

The vehicle routing problem with simultaneous pick-up and deliveries, which considers simultaneous distribution and collection of goods to/from customers, is an extension of the capacitated vehicle routing problem. There are various real cases, where fleet of vehicles originated in a depot serves customers with pick-up and deliveries from/to their locations. Increasing importance of reverse logistics activities make it necessary to determine efficient and effective vehicle routes for simultaneous pick-up and delivery activities. The vehicle routing problem with simultaneous pick-up and deliveries is also NP-hard as a capacitated vehicle routing problem and this study proposes a genetic algorithm based approach to this problem. Computational example is presented with parameter settings in order to illustrate the proposed approach. Moreover, performance of the proposed approach is evaluated by solving several test problems.     

一种基于免疫原理的多目标优化方法

借鉴生物免疫原理中抗体多样性产生及保持的机理，建立了一种多目标优化方法．该方法定义了多目标选择熵和浓度调节选择概率的概念，采用了抗体克隆选择策略和高度变异策略．最后采用四种典型的多目标优化函数，将本方法同几种常用的多目标遗传算法进行了比较研究，证明了所建立的基于免疫原理的多目标优化方法能有效解决多目标优化问题且具有一定的优越性．     

Approximate solutions for the capacitated arc routing problem

The capacitated arc routing problem (CARP), is a capacitated variation of the arc routing problems in which there is a capacity constraint associated with each vehicle. Due to the computational complexity of the problem, recent research has focussed on developing and testing heuristic algorithms which solve the CARP approximately. In this paper, we review some of the existing solution procedures, analyze their complexity, and present two modifications of the existing methods to obtain near-optimal solutions for the CARP. Extensive computational results are presented and analyzed.     

A hybrid algorithm for the capacitated vehicle routing problem with three-dimensional loading constraints

The capacitated vehicle routing problem with three-dimensional loading constraints combines capacitated vehicle routing and three-dimensional loading with additional packing constraints concerning, for example, unloading operations. An efficient hybrid algorithm including a tabu search algorithm for routing and a tree search algorithm for loading is introduced. Computational results are presented for all publicly available test instances. Most of the best solutions previously reported in literature have been improved while the computational effort is drastically reduced compared to other methods.     

基于路径切割和自适应检测的大规模限量弧路由问题求解

大规模限量弧路由问题(large scale capacitated arc routing problem, LSCARP)是一个组合优化问题,应用广泛,采用分治策略是解决LSCARP的有效方法之一.首先,为了利用分治策略取得更优的分解结果,提出改进路径切割算子来求解LSCARP,其能够自动识别路径集合中形态较差的路径并对其有针对性地进行切割,从而在迭代中通过将切割后的路径进行重组以获得更优的分解,有利于算法跳出局部最优取得更小的最终费用;然后,针对LSCARP的结构会影响算法最终效果的问题,设计一种自适应数据集检测算子,其能够根据LSCARP中任务边与非任务边的关系来进行参数分配从而提高分解质量;最后,将以上2个算子应用于SHAiD算法,并与当前主流相关算法进行对比.实验结果表明了所提出算法的有效性.     

A guided local search procedure for the multi-compartment capacitated arc routing problem

In this paper, we introduce and study the multi-compartment capacitated arc routing problem—an extension of the classical capacitated arc routing problem, but where the required edges have a demand for different products, and multi-compartment vehicles are available to co-distribute these commodities. We present a local search algorithm that exploits well-known moves (2-opt, re-insert, relocate, exchange and cross). We take advantage of speed up tricks such as marking and neighbour lists, and we combine the procedure with the guided local search meta-heuristic in order to reach high quality solutions. We report on results from extensive computational experiments. Our aim is to reveal in what situations co-distribution by partitioned vehicles saves in routing costs as compared with separate distribution with un-partitioned trucks. We explore sensitivities in key problem characteristics including, the number of commodities, the vehicle capacity, the location of the depot and required edges, the density of required edges, and the demand per commodity for the required edges.     

Mix-opt: A new route operator for optimal coverage path planning for a fleet in an agricultural environment

To accomplish agriculture tasks, a field is usually divided into tracks based on the implement width. The order in which the crop tracks are covered during this process is critical because it directly affects the distances travelled by the agricultural machines while completing the task and, consequently, soil compaction and inputs. Identifying the best tracks for a set of vehicles to completely cover a field can be formulated as a capacitated vehicle routing problem (CVRP), in which tracks can be viewed as the customers of the CVRP problem. In other words, given a set of n tracks and m vehicles, the objective is to determine a set of routes such that each track is covered exactly once by any of the involved vehicles while minimizing the total cost of covering all the tracks. There are many metaheuristic optimisation methods that address the CVRP problem by using operators to iteratively improve the routes. Most of these operators consist of easy elementary operations such as relocations, swaps or inversions of the order in which customers in the route are visited. In this paper, a new operator, named Mix-opt, is proposed with the aim of accelerating the convergence of metaheuristic optimisation methods and make them less dependent on the operator chosen on routing problems. The proposed operator combines and extends some of the features of the most commonly used route operators by integrating the best-performing elementary operations on which they are based. Further variants of those elementary operations were tested, such as the use of different numbers of elements in the relocations or swaps or reverse orders as well as combining the operations with local searches. The best variants were selected for integration into the proposed operator. Furthermore, Mix-opt was compared against well-established operators by integrating each of them into a Simulated Annealing algorithm and solving well-known CVRP benchmarks and a typical and complex agricultural routing problem. Finally, the proposed operator was applied to be integrated into an agricultural route planner to identify the best routes in some illustrative agricultural problems.All tests demonstrated that Mix-opt, on average, outperforms existing approaches for solving general routing problems as well as a broad spectrum of agricultural routing situations. This helps to better route plan in agricultural contexts, even better than other approaches in a very short time, which is interesting to route plan in real time, for example, because one vehicle may fail during the execution and then it is necessary to route the plan again and very fast to distribute the remaining part of the global task among the rest of the vehicles in the fleet.     

一种求解车间作业调度问题的免疫算法

人工免疫系统是基于生物免疫系统特性而发展的新兴智能系统。基于免疫系统的克隆选择机制,提出一种求解车间作业调度问题的免疫算法。利用免疫算法较强的搜索能力可以实现全局寻优。通过使用克隆、高频变异和抗体抑制等免疫操作,提高了算法的收敛速度和种群的多样性,可以有效地克服遗传算法种群早熟化和收敛速度慢的问题。仿真结果表明,与改进后的遗传算法比较,提出的免疫算法在全局最优解和收敛速度上都有较为明显的优势。