2,608 research outputs found
Ant colony optimization and its application to the vehicle routing problem with pickups and deliveries
Ant Colony Optimization (ACO) is a population-based metaheuristic that can be used to find approximate solutions to difficult optimization problems. It was first introduced for solving the Traveling Salesperson Problem. Since then many implementations of ACO have been proposed for a variety of combinatorial optimization. In this chapter, ACO is applied to the Vehicle Routing Problem with Pickup and Delivery (VRPPD). VRPPD determines a set of vehicle routes originating and ending at a single depot and visiting all customers exactly once. The vehicles are not only required to deliver goods but also to pick up some goods from the customers. The objective is to minimize the total distance traversed. The chapter first provides an overview of ACO approach and presents several implementations to various combinatorial optimization problems. Next, VRPPD is described and the related literature is reviewed, Then, an ACO approach for VRPPD is discussed. The approach proposes a new visibility function which attempts to capture the âdeliveryâ and âpickupâ nature of the problem. The performance of the approach is tested using well-known benchmark problems from the literature
Ant colony optimization approach for the capacitated vehicle routing problem with simultaneous delivery and pick-up
We propose an Ant Colony Optimization (ACO) algorithm to the NPhard Vehicle Routing Problem with Simultaneous Delivery and Pick-up (VRPSDP). In VRPSDP, commodities are delivered to customers from a single depot utilizing a fleet of identical vehicles and empty packages are collected from the customers and transported back to the depot. The objective is to minimize the total distance traveled. The algorithm is tested with the well-known benchmark problems from the literature. The experimental study indicates that our approach produces comparable results to those of the benchmark problems in the literature
Finding groups in data: Cluster analysis with ants
Wepresent in this paper a modification of Lumer and Faietaâs algorithm for data clustering. This approach
mimics the clustering behavior observed in real ant colonies. This algorithm discovers automatically
clusters in numerical data without prior knowledge of possible number of clusters. In this paper we focus
on ant-based clustering algorithms, a particular kind of a swarm intelligent system, and on the effects on
the final clustering by using during the classification differentmetrics of dissimilarity: Euclidean, Cosine,
and Gower measures. Clustering with swarm-based algorithms is emerging as an alternative to more
conventional clustering methods, such as e.g. k-means, etc. Among the many bio-inspired techniques, ant
clustering algorithms have received special attention, especially because they still require much
investigation to improve performance, stability and other key features that would make such algorithms
mature tools for data mining.
As a case study, this paper focus on the behavior of clustering procedures in those new approaches.
The proposed algorithm and its modifications are evaluated in a number of well-known benchmark
datasets. Empirical results clearly show that ant-based clustering algorithms performs well when
compared to another techniques
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