4,664 research outputs found
A hybrid heuristic solving the traveling salesman problem
This paper presents a new hybrid heuristic for solving the Traveling Salesman Problem, The
algorithm is designed on the frame of a general optimization procedure which acts upon two steps,
iteratively. In first step of the global search, a feasible tour is constructed based on insertion approach.
In the second step the feasible tour found at the first step, is improved by a local search optimization
procedure. The second part of the paper presents the performances of the proposed heuristic algorithm, on
several test instances. The statistical analysis shows the effectiveness of the local search optimization
procedure, in the graphical representation.peer-reviewe
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
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
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Implementation and Evaluation of Novel Buildstyles in Fused Deposition Modeling (FDM)
Previous investigations have shown that the optimization of extrusion dynamics in .conjunction with the buildstyle pattern is of paramount importance to increase part quality in
Fused Deposition Modeling (FDM). Recently domain decomposition and space filling
curves have been introduced for slice generation in FDM [1]. The current work focuses
on the implementations of fractal-like buildstyle .patterns using. Simulated Annealing [2,
3], Lin-Kernighan algorithms [4] and Construction Procedures based on Nearest
Neighbor Heuristics [5]. These computational optimization procedures are able to
generate filling patterns that allow the continuous deposition of a single road to fill arbitrary shaped domains. The necessary software modules to produce arbitrary threedimensional artifacts have been developed and are evaluated with respect to part quality
and build time.Mechanical Engineerin
Optimized annealing of traveling salesman problem from the nth-nearest-neighbor distribution
We report a new statistical general property in traveling salesman problem,
that the th-nearest-neighbor distribution of optimal tours verifies with
very high accuracy an exponential decay as a function of the order of neighbor
. With defining the energy function as the deviation from this
exponential decay, which is different to the tour length in normal
annealing processes, we propose a distinct highly optimized annealing scheme
which is performed in -space and -space by turns. The simulation
results of some standard traveling salesman problems in TSPLIB95 are presented.
It is shown that our annealing recipe is superior to the canonical simulated
annealing.Comment: 11 pages, 3 figures, preprin
An ACO-Inspired, Probabilistic, Greedy Approach to the Drone Traveling Salesman Problem
In recent years, major companies have done research on using drones for parcel delivery. Research has shown that this can result in significant savings, which has led to the formulation of various truck and drone routing and scheduling optimization problems. This paper explains and analyzes a new approach to the Drone Traveling Salesman Problem (DTSP) based on ant colony optimization (ACO).
The ACO-based approach has an acceptance policy that maximizes the usage of the drone. The results reveal that the pheromone causes the algorithm to converge quickly to the best solution. The algorithm performs comparably to the MIP model, CP model, and EA of Rich & Ham (2018), especially in instances with a larger number of stops
Exact algorithms for the order picking problem
Order picking is the problem of collecting a set of products in a warehouse
in a minimum amount of time. It is currently a major bottleneck in supply-chain
because of its cost in time and labor force. This article presents two exact
and effective algorithms for this problem. Firstly, a sparse formulation in
mixed-integer programming is strengthened by preprocessing and valid
inequalities. Secondly, a dynamic programming approach generalizing known
algorithms for two or three cross-aisles is proposed and evaluated
experimentally. Performances of these algorithms are reported and compared with
the Traveling Salesman Problem (TSP) solver Concorde
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