8,228 research outputs found
The Generalized Traveling Salesman Problem solved with Ant Algorithms
A well known N P-hard problem called the Generalized Traveling Salesman
Problem (GTSP) is considered. In GTSP the nodes of a complete undirected graph
are partitioned into clusters. The objective is to find a minimum cost tour
passing through exactly one node from each cluster. An exact exponential time
algorithm and an effective meta-heuristic algorithm for the problem are
presented. The meta-heuristic proposed is a modified Ant Colony System (ACS)
algorithm called Reinforcing Ant Colony System (RACS) which introduces new
correction rules in the ACS algorithm. Computational results are reported for
many standard test problems. The proposed algorithm is competitive with the
other already proposed heuristics for the GTSP in both solution quality and
computational time.Comment: indexed in Scopus, ORCI
Apply Local Clustering Method to Improve the Running Speed of Ant Colony Optimization
Ant Colony Optimization (ACO) has time complexity O(t*m*N*N), and its typical
application is to solve Traveling Salesman Problem (TSP), where t, m, and N
denotes the iteration number, number of ants, number of cities respectively.
Cutting down running time is one of study focuses, and one way is to decrease
parameter t and N, especially N. For this focus, the following method is
presented in this paper. Firstly, design a novel clustering algorithm named
Special Local Clustering algorithm (SLC), then apply it to classify all cities
into compact classes, where compact class is the class that all cities in this
class cluster tightly in a small region. Secondly, let ACO act on every class
to get a local TSP route. Thirdly, all local TSP routes are jointed to form
solution. Fourthly, the inaccuracy of solution caused by clustering is
eliminated. Simulation shows that the presented method improves the running
speed of ACO by 200 factors at least. And this high speed is benefit from two
factors. One is that class has small size and parameter N is cut down. The
route length at every iteration step is convergent when ACO acts on compact
class. The other factor is that, using the convergence of route length as
termination criterion of ACO and parameter t is cut down.Comment: 21 pages, 5figure
Research on the mobile robots intelligent path planning based on ant colony algorithm application in manufacturing logistics
With the development of robotics and artificial intelligence field
unceasingly thorough, path planning as an important field of robot calculation
has been widespread concern. This paper analyzes the current development of
robot and path planning algorithm and focuses on the advantages and
disadvantages of the traditional intelligent path planning as well as the path
planning. The problem of mobile robot path planning is studied by using ant
colony algorithm, and it also provides some solving methods.Comment: 17 pages,7 figure
A Unifying Survey of Reinforced, Sensitive and Stigmergic Agent-Based Approaches for E-GTSP
The Generalized Traveling Salesman Problem (GTSP) is one of the NP-hard
combinatorial optimization problems. A variant of GTSP is E-GTSP where E,
meaning equality, has the constraint: exactly one node from a cluster of a
graph partition is visited. The main objective of the E-GTSP is to find a
minimum cost tour passing through exactly one node from each cluster of an
undirected graph. Agent-based approaches involving are successfully used
nowadays for solving real life complex problems. The aim of the current paper
is to illustrate some variants of agent-based algorithms including ant-based
models with specific properties for solving E-GTSP.Comment: 9 pages, 2 figure
Physarum-inspired Network Optimization: A Review
The popular Physarum-inspired Algorithms (PAs) have the potential to solve
challenging network optimization problems. However, the existing researches on
PAs are still immature and far from being fully recognized. A major reason is
that these researches have not been well organized so far. In this paper, we
aim to address this issue. First, we introduce Physarum and its intelligence
from the biological perspective. Then, we summarize and group four types of
Physarum-inspired networking models. After that, we analyze the network
optimization problems and applications that have been challenged by PAs based
on these models. Ultimately, we discuss the existing researches on PAs and
identify two fundamental questions: 1) What are the characteristics of Physarum
networks? 2) Why can Physarum solve some network optimization problems?
Answering these two questions is essential to the future development of
Physarum-inspired network optimization.Comment: Physarum polycephalum; nature-inspired algorithm; data analytic
Apply Ant Colony Algorithm to Search All Extreme Points of Function
To find all extreme points of multimodal functions is called extremum
problem, which is a well known difficult issue in optimization fields. Applying
ant colony optimization (ACO) to solve this problem is rarely reported. The
method of applying ACO to solve extremum problem is explored in this paper.
Experiment shows that the solution error of the method presented in this paper
is less than 10^-8. keywords: Extremum Problem; Ant Colony Optimization (ACO)Comment: 23 pages, 7 figure
Fine-tuning the Ant Colony System algorithm through Particle Swarm Optimization
Ant Colony System (ACS) is a distributed (agent- based) algorithm which has
been widely studied on the Symmetric Travelling Salesman Problem (TSP). The
optimum parameters for this algorithm have to be found by trial and error. We
use a Particle Swarm Optimization algorithm (PSO) to optimize the ACS
parameters working in a designed subset of TSP instances. First goal is to
perform the hybrid PSO-ACS algorithm on a single instance to find the optimum
parameters and optimum solutions for the instance. Second goal is to analyze
those sets of optimum parameters, in relation to instance characteristics.
Computational results have shown good quality solutions for single instances
though with high computational times, and that there may be sets of parameters
that work optimally for a majority of instances.Comment: 2006 paper. Presented in conference. Technical report in "Universitat
de Valencia
Integrating Fuzzy and Ant Colony System for Fuzzy Vehicle Routing Problem with Time Windows
In this paper fuzzy VRPTW with an uncertain travel time is considered.
Credibility theory is used to model the problem and specifies a preference
index at which it is desired that the travel times to reach the customers fall
into their time windows. We propose the integration of fuzzy and ant colony
system based evolutionary algorithm to solve the problem while preserving the
constraints. Computational results for certain benchmark problems having short
and long time horizons are presented to show the effectiveness of the
algorithm. Comparison between different preferences indexes have been obtained
to help the user in making suitable decisions
A hybrid exact-ACO algorithm for the joint scheduling, power and cluster assignment in cooperative wireless networks
Base station cooperation (BSC) has recently arisen as a promising way to
increase the capacity of a wireless network. Implementing BSC adds a new design
dimension to the classical wireless network design problem: how to define the
subset of base stations (clusters) that coordinate to serve a user. Though the
problem of forming clusters has been extensively discussed from a technical
point of view, there is still a lack of effective optimization models for its
representation and algorithms for its solution. In this work, we make a further
step towards filling such gap: 1) we generalize the classical network design
problem by adding cooperation as an additional decision dimension; 2) we
develop a strong formulation for the resulting problem; 3) we define a new
hybrid solution algorithm that combines exact large neighborhood search and ant
colony optimization. Finally, we assess the performance of our new model and
algorithm on a set of realistic instances of a WiMAX network.Comment: This is the author's final version of the paper published in G. Di
Caro, G. Theraulaz (eds.), BIONETICS 2012: Bio-Inspired Models of Network,
Information, and Computing Systems. LNICST, vol. 134, pp. 3-17. Springer,
Heidelberg, 2014, DOI: 10.1007/978-3-319-06944-9_1 ). The final publication
is available at Springer via http://dx.doi.org/10.1007/978-3-319-06944-9_
All Colors Shortest Path Problem
All Colors Shortest Path problem defined on an undirected graph aims at
finding a shortest, possibly non-simple, path where every color occurs at least
once, assuming that each vertex in the graph is associated with a color known
in advance. To the best of our knowledge, this paper is the first to define and
investigate this problem. Even though the problem is computationally similar to
generalized minimum spanning tree, and the generalized traveling salesman
problems, allowing for non-simple paths where a node may be visited multiple
times makes All Colors Shortest Path problem novel and computationally unique.
In this paper we prove that All Colors Shortest Path problem is NP-hard, and
does not lend itself to a constant factor approximation. We also propose
several heuristic solutions for this problem based on LP-relaxation, simulated
annealing, ant colony optimization, and genetic algorithm, and provide
extensive simulations for a comparative analysis of them. The heuristics
presented are not the standard implementations of the well known heuristic
algorithms, but rather sophisticated models tailored for the problem in hand.
This fact is acknowledged by the very promising results reported
- …