Parallel ACO with a Ring Neighborhood for Dynamic TSP

The current paper introduces a new parallel computing technique based on ant colony optimization for a dynamic routing problem. In the dynamic traveling salesman problem the distances between cities as travel times are no longer fixed. The new technique uses a parallel model for a problem variant that allows a slight movement of nodes within their Neighborhoods. The algorithm is tested with success on several large data sets.Comment: 8 pages, 1 figure; accepted J. Information Technology Researc

Parallel local search for the time-constrained traveling salesman problem

In the time-constrained TSP, each city has to be visited within a given time interval. Such `time windows' often occur in practice. When practical vehicle routing problems are solved in an interactive setting, one needs algorithms for the time-constrained TSP that combine a low running time with a high solution quality. Local search seems a natural approach. It is not obvious, however, how local search for the TSP has to be implemented so as to handle time windows efficiently. This is particularly true when parallel computer architectures are available. We consider these questions

Sequential and parallel local search for the time-constrained travelling salesman problem

Local search has proven to be an effective solution approach for the traveling salesman problem. We consider variants of the TSP in which each city is to be visited within one or more given time windows. The travel times are symmetric and satisfy the triangle inequality; therobjective is to minimize the tour duration. We develop efficient sequential and parallel algorithms for the verification of local optimality of a tour with respect to k-exchanges

A parallel implementation of ant colony optimization

Ant Colony Optimization is a relatively new class of meta-heuristic search techniques for optimization problems. As it is a population-based technique that examines numerous solution options at each step of the algorithm, there are a variety of parallelization opportunities. In this paper, several parallel decomposition strategies are examined. These techniques are applied to a specific problem, namely the travelling salesman problem, with encouraging speedup and efficiency results.Full Tex

An improved Ant Colony System for the Sequential Ordering Problem

It is not rare that the performance of one metaheuristic algorithm can be improved by incorporating ideas taken from another. In this article we present how Simulated Annealing (SA) can be used to improve the efficiency of the Ant Colony System (ACS) and Enhanced ACS when solving the Sequential Ordering Problem (SOP). Moreover, we show how the very same ideas can be applied to improve the convergence of a dedicated local search, i.e. the SOP-3-exchange algorithm. A statistical analysis of the proposed algorithms both in terms of finding suitable parameter values and the quality of the generated solutions is presented based on a series of computational experiments conducted on SOP instances from the well-known TSPLIB and SOPLIB2006 repositories. The proposed ACS-SA and EACS-SA algorithms often generate solutions of better quality than the ACS and EACS, respectively. Moreover, the EACS-SA algorithm combined with the proposed SOP-3-exchange-SA local search was able to find 10 new best solutions for the SOP instances from the SOPLIB2006 repository, thus improving the state-of-the-art results as known from the literature. Overall, the best known or improved solutions were found in 41 out of 48 cases.Comment: 30 pages, 8 tables, 11 figure