8,228 research outputs found

    The Generalized Traveling Salesman Problem solved with Ant Algorithms

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    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

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    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

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    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

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    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

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    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

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    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

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    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

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    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

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    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

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    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
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