92,600 research outputs found
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
An Efficient Hybrid Ant Colony System for the Generalized Traveling Salesman Problem
The Generalized Traveling Salesman Problem (GTSP) is an extension of the
well-known Traveling Salesman Problem (TSP), where the node set is partitioned
into clusters, and the objective is to find the shortest cycle visiting each
cluster exactly once. In this paper, we present a new hybrid Ant Colony System
(ACS) algorithm for the symmetric GTSP. The proposed algorithm is a
modification of a simple ACS for the TSP improved by an efficient GTSP-specific
local search procedure. Our extensive computational experiments show that the
use of the local search procedure dramatically improves the performance of the
ACS algorithm, making it one of the most successful GTSP metaheuristics to
date.Comment: 7 page
Accelerated Stochastic Sampling of Discrete Statistical Systems
We propose a method to reduce the relaxation time towards equilibrium in
stochastic sampling of complex energy landscapes in statistical systems with
discrete degrees of freedom by generalizing the platform previously developed
for continuous systems. The method starts from a master equation, in contrast
to the Fokker-Planck equation for the continuous case. The master equation is
transformed into an imaginary-time Schr\"odinger equation. The Hamiltonian of
the Schr\"odinger equation is modified by adding a projector to its known
ground state. We show how this transformation decreases the relaxation time and
propose a way to use it to accelerate simulated annealing for optimization
problems. We implement our method in a simplified kinetic Monte Carlo scheme
and show an acceleration by an order of magnitude in simulated annealing of the
symmetric traveling salesman problem. Comparisons of simulated annealing are
made with the exchange Monte Carlo algorithm for the three-dimensional Ising
spin glass. Our implementation can be seen as a step toward accelerating the
stochastic sampling of generic systems with complex landscapes and long
equilibration times.Comment: 18 pages, 6 figures, to appear in Phys. Rev.
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