Two‐Layer Location‐Routing Problem Based on Heuristic Hybrid Algorithm

An image segmentation method based on a modified local-information weighted intuitionistic Fuzzy C-means clustering and Gold-panning Algorithm

Image segmentation plays an important role in machine vision, image recognition, and imaging applications. Based on the fuzzy c-means clustering algorithm, a kernel-distance-based intuitionistic fuzzy c-means clustering (KIFCM) algorithm is proposed. First, a fuzzy complement operator is used to generate the membership degree whereby the hesitation degree of intuitionistic fuzzy set is generated; second, a kernel-induced function is used to calculate the distance from each point to the cluster center instead of the Euclidean distance; third, a new objective function that includes the hesitation degree is established, and the optimization of the objective function results in new iterative expressions for the membership degree and the cluster center. The proposed KIFCM algorithm is compared with the fuzzy c-means clustering (FCM) algorithm, the kernel fuzzy c-means clustering (KFCM) algorithm, and the intuitionistic fuzzy c-means clustering (IFCM) algorithm in segmenting five images. The experimental results verify the effectiveness and superiority of our proposed KIFCM algorithm.

In this study, the concepts of competitive agglomeration clustering algorithm is incorporated into fuzzy c-means (FCM) clustering algorithm for symbolic interval-values data. In the proposed approach, called as IFCMwUNC clustering algorithm, the problems of the unknown clusters number and the initialization of prototypes in the FCM clustering algorithm for symbolic interval-values data are overcome and discussed. Due to the competitive agglomeration clustering algorithm possess the advantages of the hierarchical clustering algorithm and the partitional clustering algorithm, IFCMwUNC clustering algorithm can be fast converges in a few iterations regardless of the initial number of clusters. Moreover, it is also converges to the same optimal partition regardless of its initialization. Experiments results show the merits and usefulness of IFCMwUNC clustering algorithm for the symbolic interval-values data.

Traditional library systems are gradually being replaced by digital libraries. Digital libraries are developing from simple database-based storage and retrieval to knowledge-based implementation. The fuzzy C-means (FCM) clustering algorithm is an example of data collection and data processing technology. It evaluates and draws conclusions based on mathematics, large data, and other technologies. In order to better improve the digital library management system, this paper applied FCM clustering algorithm to the digital library management system. Based on the in-depth study of the FCM clustering algorithm, this paper built a digital library management system. The clustering algorithm was used to cluster library borrowing records and reader information. It provided technical support and suggestions on library collection construction and book purchase and promoted book management to form a good spitting cycle. The experimental results extracted during the evaluation phase demonstrated that the overall error rate of the suggested FCM clustering algorithm for information clustering is 3.66%, which is better than the existing comparative models. This shows that applying the FCM clustering algorithm to a digital library management system has some practical significance.

The multiple traveling salesman problem: an overview of formulations and solution procedures

The location routing problem (LRP), which simultaneously tackles both facility location and the vehicle routing decisions to minimize the total system cost, is of great importance in designing an integrated logistic distribution network. In this paper a simulated annealing algorithm (SA) based hybrid genetic algorithm was developed to solve the LRP with capacity constraints (CLRP) on depots and routes. The proposed hybrid genetic algorithm modified the population generation method, genetic operators and recombination strategy and realized the combination of the local searching ability of SA and global searching ability of GA. To evaluate the performance of the proposed approach, we conducted an experimental study and compared its results with other heuristics on a set of well-known Barreto Benchmark instances. The experimental results verified the feasibility and effectiveness of our approach.

Many components of intelligent transportation systems require different levels of the traffic flow forecasting. This paper presents a novel short-term traffic flow forecasting model using a distributed radial basis function neural network (RBFNN) based on adaptive fuzzy c-means (FCM) clustering algorithm. FCM clustering algorithm is used to classify training objects into a couple of clusters, each cluster is trained by a sub RBFNN, and membership values are used for combining several RBFNN outputs to obtain the final result. In the online stage, the membership values are computed using an adaptive fuzzy clustering algorithm for the new object. The real traffic data are used to demonstrate the effectiveness of the method.

Fuzzy C-means (FCM) clustering algorithm is an important and popular clustering algorithm which is utilized in various application domains such as pattern recognition, machine learning, and data mining. Although this algorithm has shown acceptable performance in diverse problems, the current literature does not have studies about how they can improve the clustering quality of partitions with overlapping classes. The better the clustering quality of a partition, the better is the interpretation of the data, which is essential to understand real problems. This work proposes two robust FCM algorithms to prevent ambiguous membership into clusters. For this, we compute two types of weights: an weight to avoid the problem of overlapping clusters; and other weight to enable the algorithm to identify clusters of different shapes. We perform a study with synthetic datasets, where each one contains classes of different shapes and different degrees of overlapping. Moreover, the study considered real application datasets. Our results indicate such weights are effective to reduce the ambiguity of membership assignments thus generating a better data interpretation.

Firstly, the paper makes a briefly analysis and comment about the fuzzy c-means clustering algorithm. Then a new kind of hybrid genetic algorithm is proposed on the base of the combination of genetic algorithm and simulated annealing algorithm, and it is applied in fuzzy c-means clustering. It overcomes the locality and the Sensitivity to initial clustering central of fuzzy c-means clustering, by using randomness and parallelism in hybrid genetic algorithm searching. And a new tree-shaped coding scheme adapted to fuzzy clustering is adopted in the genetic algorithm. In the end, the paper supplies the detailed design of the method. Simulation experiments show the relatively high efficiency and recognition accuracy of the method, which has extensive application prospect in many fields, such as pattern recognition, data mining, and so on.

Disasters are events that disturb and threaten people's lives caused by nature or/and non-natural factors as well as human factors that cause casualties and economic losses. The polemic of uneven assistance and delays in the event of a natural disaster is the most common thing that occurs during a natural disaster. Location Routing Problem is a continuation of the classical routing problem that combines strategic and operational decisions with the facility location problem and the vehicle routing problem. This study aims to determine the location of the distribution centre construction and the optimal route using the Hybrid Genetic Algorithm and Simulated Annealing methods with the objective function of minimizing total costs and minimizing maximum service time for the distribution of humanitarian aid natural disaster 2018 in Sigi Regency. Optimization is designed into two scenarios, namely the construction of two distribution centres and three distribution centres. The result show that construction of two distribution centres can be designed at locations D3 and D5 with a total cost of IDR405 609 000 and a maximum travel time of 25.921 hours, while the construction of three distribution centres can be done at locations DC1, DC4 and DC 5 or with a total cost of IDR605,778,000 and a maximum service time of 19.634 hours.

The Multiple Travelling Salesman Problem (MTSP) is a basic deformation of the Travelling Salesman Problem (TSP), which is a typical NP-Hard problem. For combinatorial optimization problems shaped like MTSP, researchers often use intelligent optimization algorithms such as genetic algorithms, differential evolutionary algorithms, simulated annealing algorithms and other intelligent optimization algorithms to approximate the solution. However, typical intelligent optimization algorithms have the disadvantage of being prone to local convergence and failing to produce theoretically optimal solutions. Based on this, we propose an HCA&DE algorithm that improves the DE algorithm by using the hill-climbing algorithm, and carry out data experiments using the solution set of TSPLIB, which proves the practicality and effectiveness of the algorithm.

A good exploration ability can ensure that the method jumps out of local optimum in multimodal problems and a good exploitation can ensure an algorithm converge faster to global optimum values. So, this paper proposes a new hybrid sperm swarm optimization and genetic algorithm to obtain global optimal solutions termed HSSOGA which is developed based on the concept of balancing the exploration and exploitation capability by merging Sperm Swarm Optimization (SSO), which has a fast convergence rate, and a Genetic Algorithm (GA) that can explore a search domain efficiently. To ensure that the proposed method delivers good performance, it is evaluated with 11 standard test function problems consisting of 5 unimodal and 6 multimodal functions. The proposed HSSOGA set is compared with conventional GA and SSO methods, as well as with several hybrid methods such as Hybrid Firefly and Particle Swarm Optimization (HFPSO), hybrid Simulated Annealing and Genetic Algorithm (SAGA), Hybrid Particle Swarm Optimization and Genetic Algorithm (HFPSO), hybrid Particle Swarm Optimization and Grey Wolf Optimization (PSOGWO), and closely related Hybrid Sperm Swarm Optimization and Gravitational Search Algorithm (HSSOGSA). The results are evaluated in terms of each method’s best fitness, mean, standard deviation, and convergence rates. The numerical experiment results show that HSSOGA has better convergence towards the true global optimum values as compared to the conventional and existing hybrid methods in most unimodal and multimodal test function problems.

Gold immunochromatographic strip (GICS) quantitative detective can provide more information than the qualitative or semiquantitative testing. In this paper, a fast color image segmentation method is presented to develop the quantitative detective of GICS. The image of GICS was acquired by Charge-coupled Device (CCD) image sensor, and segmented by fuzzy c-means (FCM) clustering algorithm based on color histogram in HSV color space. Maximin-distance algorithm was adopted to get the initial positions of centroids and cluster number to overcome the shortcoming that the FCM algorithm may produce local optimal results. For the segmented target image, a special characteristic parameter was constructed and calculated in HSV color space to achieve the quantitative interpretation of the GICS.

A generalization of the well-known Vehicle Routing Problem (VRP) has been developed toward tactical or strategic decision levels of companies but not both. The tactical extension or Periodic VRP (PVRP) plans a set of trips over a multiperiod horizon, subject to frequency constraints. The strategic extension is motivated by interdependent depot location and routing decisions in most distribution systems. Low-quality solutions are obtained if depots are located first, regardless the future routes. In the Location-Routing Problem (LRP), location and routing decisions are simultaneously tackled. The goal here is to combine the PVRP and LRP into an even more realistic problem covering all decision levels: the Periodic LRP or PLRP. An evolutionary algorithm called Memetic Algorithm with Population Management (MA|PM) is proposed to solve large size instances of the PLRP. First, a population is randomly generated. Every individual represents a feasible solution using the same combination of visit days on each customers. The evolution is operated by a memetic mechanism and the offsprings must satisfy a distance test before entering the population. Information about better customer assignment to visit days is collected on the offsprings, and is used to create a new population of solutions. The algorithm stops when a given number of regenerations of the population is reached. The method is evaluated on three sets of instances and solutions are compared to the literature on particular cases such as one-day horizons (LRP) or one depot (PVRP). This metaheuristic outperforms the previous method for the PLRP.

China,Phone:+86-02982334426,e-mail:dwhu@chd.edu.cn) Abstract: The Location Routing Problem is the integrated decision of the Location Allocation Problem and the Vehicle Routing Problem. This paper will describe the location routing problem, set up the mathematical model of the problem, and test the model by Lingo 10.0. In view of this model’s NP hard attribute, the paper designs two-stage tabu search algorithms to solve this problem, i.e. in the first phase, solve the location allocation to fix on the facility and customer allocation; in the second phase, solve the vehicle routing problem by tabu search algorithms; at last, by a large number of iterative from location to routing phases, the optimal solution of the location routing problem can be obtained. The computed result shows the validity of the designed algorithm by comparing it with related literature.