Lin-Kernighan Heuristic Adaptations for the Generalized Traveling Salesman Problem

The Lin-Kernighan heuristic is known to be one of the most successful heuristics for the Traveling Salesman Problem (TSP). It has also proven its efficiency in application to some other problems. In this paper we discuss possible adaptations of TSP heuristics for the Generalized Traveling Salesman Problem (GTSP) and focus on the case of the Lin-Kernighan algorithm. At first, we provide an easy-to-understand description of the original Lin-Kernighan heuristic. Then we propose several adaptations, both trivial and complicated. Finally, we conduct a fair competition between all the variations of the Lin-Kernighan adaptation and some other GTSP heuristics. It appears that our adaptation of the Lin-Kernighan algorithm for the GTSP reproduces the success of the original heuristic. Different variations of our adaptation outperform all other heuristics in a wide range of trade-offs between solution quality and running time, making Lin-Kernighan the state-of-the-art GTSP local search.Comment: 25 page

Solving the extended vehicle scheduling problem with metaheuristics

Mestrado Integrado. Engenharia Informática e Computação. Faculdade de Engenharia. Universidade do Porto. 201

Influence of Programming Language on the Execution Time of Ant Colony Optimization Algorithm

Supply chains can be accelerated by route optimization, a computationally intensive process for a large number of instances. Traveling Salesmen Problem, as the representative example of routing problems, is NP-hard combinatorial problem. It means that the time needed for solving the problem with exact methods increases exponentially with the increased dataset. Using metaheuristic methods, like Ant Colony Optimization, reduces the time needed for solving the problem drastically but finding a solution still takes a considerable amount of time for large datasets. In today’s dynamic environment finding the solution as fast as possible is important as finding a quality solution. The programming language used for finding the solution also influences execution time. In this paper, the execution time of Ant Colony Optimization to solve Traveling Salesman Problems of different sizes was measured. The algorithm was programmed in several programming languages, execution time was measured to rank programming languages

Efficient Block Scheduling to Minimize Context Switching Time for Programmable Embedded Processors

Scheduling is one of the most often addressed optimization problems in DSP compilation, behavioral synthesis, and system-level synthesis research. With the rapid pace of changes in modern DSP applications requirements and implementation technologies, however, new types of scheduling challenges arise. This paper is concerned with the problem of scheduling blocks of computations in order to optimize the efficiency of their execution on programmable embedded systems under a realistic timing model of their processors. We describe an effective scheme for scheduling the blocks of any computation on a given system architecture and with a specified algorithm implementing each block. We also present algorithmic techniques for performing optimal block scheduling simultaneously with optimal architecture and algorithm selection. Our techniques address the block scheduling problem for both single- and multiple-processor system platforms and for a variety of optimization objectives including throughput, cost, and power dissipation. We demonstrate the practical effectiveness of our techniques on numerous designs and synthetic examples.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/44804/1/10617_2004_Article_239764.pd

Performance of Commercial Quantum Annealing Solvers for the Capacitated Vehicle Routing Problem

Quantum annealing (QA) is a heuristic search algorithm that can run on Adiabatic Quantum Computation (AQC) processors to solve combinatorial optimization problems. Although theoretical studies and simulations on classic hardware have shown encouraging results, these analyses often assume that the computation occurs in adiabatically closed systems without environmental interference. This is not a realistic assumption for real systems; therefore, without extensive empirical measurements on real quantum platforms, theory-based predictions, simulations on classical hardware or limited tests do not accurately assess the current commercial capabilities. This study has assessed the quality of the solution provided by a commercial quantum annealing platform compared to known solutions for the Capacitated Vehicle Routing Problem (CVRP). The study has conducted extensive analysis over more than 30 hours of access to QA commercial platforms to investigate how the size of the problem and its complexity impact the solution accuracy and the time used to find a solution. Our results have found that the absolute error is between 0.12 and 0.55, and the quantum processor unit (QPU) time is between 30 and 46 micro seconds. Our results show that as the constraint density increases, the quality of the solution degrades. Therefore, more than the problem size, the model complexity plays a critical role, and practical applications should select formulations that minimize the constraint density

A Parallel Meta-Heuristic Approach to Reduce Vehicle Travel Time in Smart Cities

The development of the smart city concept and inhabitants’ need to reduce travel time, in addition to society’s awareness of the importance of reducing fuel consumption and respecting the environment, have led to a new approach to the classic travelling salesman problem (TSP) applied to urban environments. This problem can be formulated as “Given a list of geographic points and the distances between each pair of points, what is the shortest possible route that visits each point and returns to the departure point?”. At present, with the development of Internet of Things (IoT) devices and increased capabilities of sensors, a large amount of data and measurements are available, allowing researchers to model accurately the routes to choose. In this work, the aim is to provide a solution to the TSP in smart city environments using a modified version of the metaheuristic optimization algorithm Teacher Learner Based Optimization (TLBO). In addition, to improve performance, the solution is implemented by means of a parallel graphics processing unit (GPU) architecture, specifically a Compute Unified Device Architecture (CUDA) implementation.This research was supported by the Spanish Ministry of Science, Innovation and Universities and the Research State Agency under Grant RTI2018-098156-B-C54 co-financed by FEDER funds, and by the Spanish Ministry of Economy and Competitiveness under Grant TIN2017-89266-R, co-financed by FEDER funds

Enhancing the Usefulness of Blockchain Technology in Finance Sector

Blockchain technology has become widely popular with the appearance of cryptocurrencies that use the decentralized nature of blockchain in order to exchange funds between their users. In order to verify various needed details during an exchange, consensus mechanisms are used which solve simple but exhaustive calculations. Such operations fulfil their primary goal of verifying, but are a common target of public disapproval due to massive energy consumption and lack of usefulness. This work discusses different approaches and consensus mechanisms with a more useful secondary function, especially focusing on NP-complete problems as mediators in solving complex and resource-heavy problems. A new way of approaching these problems can benefit many areas, like science, healthcare, government and finance, optimizing the current infrastructure and business processes like markets, transactions, insurances, payments and supply chains, or creating more secure, reliable and efficient environment. This work is licensed under a&nbsp;Creative Commons Attribution-NonCommercial 4.0 International License.</p