Significance Relations for the Benchmarking of Meta-Heuristic Algorithms

The experimental analysis of meta-heuristic algorithm performance is usually based on comparing average performance metric values over a set of algorithm instances. When algorithms getting tight in performance gains, the additional consideration of significance of a metric improvement comes into play. However, from this moment the comparison changes from an absolute to a relative mode. Here the implications of this paradigm shift are investigated. Significance relations are formally established. Based on this, a trade-off between increasing cycle-freeness of the relation and small maximum sets can be identified, allowing for the selection of a proper significance level and resulting ranking of a set of algorithms. The procedure is exemplified on the CEC'05 benchmark of real parameter single objective optimization problems. The significance relation here is based on awarding ranking points for relative performance gains, similar to the Borda count voting method or the Wilcoxon signed rank test. In the particular CEC'05 case, five ranks for algorithm performance can be clearly identified.Comment: 6 pages, 2 figures, 1 tabl

Wild Patterns Reloaded: A Survey of Machine Learning Security against Training Data Poisoning

The success of machine learning is fueled by the increasing availability of computing power and large training datasets. The training data is used to learn new models or update existing ones, assuming that it is sufficiently representative of the data that will be encountered at test time. This assumption is challenged by the threat of poisoning, an attack that manipulates the training data to compromise the model's performance at test time. Although poisoning has been acknowledged as a relevant threat in industry applications, and a variety of different attacks and defenses have been proposed so far, a complete systematization and critical review of the field is still missing. In this survey, we provide a comprehensive systematization of poisoning attacks and defenses in machine learning, reviewing more than 100 papers published in the field in the last 15 years. We start by categorizing the current threat models and attacks, and then organize existing defenses accordingly. While we focus mostly on computer-vision applications, we argue that our systematization also encompasses state-of-the-art attacks and defenses for other data modalities. Finally, we discuss existing resources for research in poisoning, and shed light on the current limitations and open research questions in this research field

Routing with Reloads

We examine routing problems with reloads, how they can be modeled, their properties and how they can be solved. We propose a simple model, the Pickup and Delivery Problem with Reloads (RPDP), that captures the process of reloading and can be extended for real world applications. We present results that show that the RPDP is solvable in polynomial time if the number of requests is bounded by a constant. Additionally, we examine a special case of the RPDP, the k-Star Hub Problem. This problem is solvable efficiently by network flow approaches if no more than two hubs are available. Otherwise, it is NP-complete. In the second part of this thesis, additional constraints are incorporated into the model and a tabu search heuristic for this problem is presented. The heuristic has been implemented and tested on several benchmarking instances, both artificial and a real-world application. In the appendix, we discuss the application of column generation for a reload problem

Routing design for less-than-truckload motor carriers using ant colony techniques

One of the most important challenges for Less-Than-Truck-Load carriers consists of determining how to consolidate flows of small shipments to minimize costs while maintaining a certain level of service. For any origin-destination pair, there are several strategies to consolidate flows, but the most usual ones are: peddling/collecting routes and shipping through one or more break-bulk terminals. Therefore, the target is determining a route for each origin-destination pair that minimizes the total transportation and handling cost guaranteeing a certain level of service. Exact resolution is not viable for real size problems due to the excessive computational time required. This research studies different aspects of the problem and provides a metaheuristic algorithm (based on Ant Colonies Optimization techniques) capable of solving real problems in a reasonable computational time. The viability of the approach has been proved by means of the application of the algorithm to a real Spanish case, obtaining encouraging results

ROUTING DESIGN FOR LESS-THAN-TRUCKLOAD MOTOR CARRIERS USING ANT COLONY TECHNIQUES

One of the most important challenges for Less-Than-Truck-Load carriers consists of determining how to consolidate flows of small shipments to minimize costs while maintaining a certain level of service. For any origin-destination pair, there are several strategies to consolidate flows, but the most usual ones are: peddling/collecting routes and shipping through one or more break-bulk terminals. Therefore, the target is determining a route for each origin-destination pair that minimizes the total transportation and handling cost guaranteeing a certain level of service. Exact resolution is not viable for real size problems due to the excessive computational time required. This research studies different aspects of the problem and provides a metaheuristic algorithm (based on Ant Colonies Optimization techniques) capable of solving real problems in a reasonable computational time. The viability of the approach has been proved by means of the application of the algorithm to a real Spanish case, obtaining encouraging results.

Cross-docking: A systematic literature review

This paper identifies the major research concepts, techniques, and models covered in the cross-docking literature. A systematic literature review is conducted using the BibExcel bibliometric analysis and Gephi network analysis tools. A research focus parallelship network (RFPN) analysis and keyword co-occurrence network (KCON) analysis are used to identify the primary research themes. The RFPN results suggest that vehicle routing, inventory control, scheduling, warehousing, and distribution are most studied. Of the optimization and simulation techniques applied in cross-docking, linear and integer programming has received much attention. The paper informs researchers interested in investigating cross-docking through an integrated perspective of the research gaps in this domain. This paper systematically reviews the literature on cross-docking, identifies the major research areas, and provides a survey of the techniques and models adopted by researchers in the areas related to cross-docking

Autonomic log/restore for advanced optimistic simulation systems

In this paper we address state recoverability in optimistic simulation systems by presenting an autonomic log/restore architecture. Our proposal is unique in that it jointly provides the following features: (i) log/restore operations are carried out in a completely transparent manner to the application programmer, (ii) the simulation-object state can be scattered across dynamically allocated non-contiguous memory chunks, (iii) two differentiated operating modes, incremental vs non-incremental, coexist via transparent, optimized run-time management of dual versions of the same application layer, with dynamic selection of the best suited operating mode in different phases of the optimistic simulation run, and (iv) determinationof the best suited mode for any time frame is carried out on the basis of an innovative modeling/optimization approach that takes into account stability of each operating mode vs variations of the model execution parameters. © 2010 IEEE

A scheme for determining vehicle routes based on Arc-based service network design

In freight transportation, less-than-truckload carriers often need to assign each vehicle a cyclic route so that drivers can come back home after a certain period of time. However, the Node-Arc model for service network design addresses decisions on each arc and does not determine routes directly, although the vehicle balancing constraint ensures that the number of outgoing vehicles equals the number of incoming vehicles at each node. How to transform the optimized service network into a set of vehicle routes remains an important problem that has not yet been studied. In this paper, we propose a three-phase scheme to address this problem. In the first stage, we present an algorithm based on the depth-first search to find all of the different cyclic routes in a service network design solution. In the second stage, we propose to prune poor cyclic routes using real-life constraints so that a collection of acceptable vehicle routes can be obtained before route assignment. Some of the pruning can also be done in the first stage to speed up the proposed algorithm. In the third stage, we formulate the problem of selecting a set of cyclic routes to cover the entire network as a weighted set covering problem. The resulting model is formulated as an integer program and solved with IBM ILOG CPLEX solver. Experimental results on benchmark instances for service network design indicate the effectiveness of the proposed scheme which gives high-quality solutions in an efficient way