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122
Pheromone Modification Strategies for Ant Algorithms applied to Dynamic TSP
, 2001
"... We investigate strategies for pheromone modification of ant algorithms in reaction to the insertion/deletion of a city of Traveling Salesperson Problem (TSP) instances. Three strategies for pheromone diversification through equalization of the pheromone values on the edges are proposed and compared. ..."
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Cited by 44 (1 self)
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We investigate strategies for pheromone modification of ant algorithms in reaction to the insertion/deletion of a city of Traveling Salesperson Problem (TSP) instances. Three strategies for pheromone diversification through equalization of the pheromone values on the edges are proposed and compared. One strategy acts globally without consideration of the position of the inserted/deleted city. The other strategies perform pheromone modification only in the neighborhood of the inserted/deleted city, where neighborhood is defined differently for the two strategies. We furthermore evaluate di erent parameter settings for each of the strategies.
A Population Based Approach for ACO
, 2002
"... A population based ACO (Ant Colony Optimization) algorithm is proposed where (nearly) all pheromone information corresponds to solutions that are members of the actual population. Advantages of the population based approach are that it seems promising for solving dynamic optimization problems, i ..."
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Cited by 38 (7 self)
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A population based ACO (Ant Colony Optimization) algorithm is proposed where (nearly) all pheromone information corresponds to solutions that are members of the actual population. Advantages of the population based approach are that it seems promising for solving dynamic optimization problems, its nite state space and the chances it oers for designing new metaheuristics. We compare the behavior of the new approach to the standard ACO approach for several instances of the TSP and the QAP problem. The results show that the new approach is competitive.
Ants can solve Constraint Satisfaction Problems
 IEEE Transactions on Evolutionary Computation
, 2001
"... In this paper we describe a new incomplete approach for solving constraint satisfaction problems (CSPs) based on the ant colony optimization (ACO) metaheuristic. The idea is to use artificial ants to keep track of promising areas of the search space by laying trails of pheromone. This pheromone info ..."
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Cited by 32 (11 self)
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In this paper we describe a new incomplete approach for solving constraint satisfaction problems (CSPs) based on the ant colony optimization (ACO) metaheuristic. The idea is to use artificial ants to keep track of promising areas of the search space by laying trails of pheromone. This pheromone information is used to guide the search, as a heuristic for choosing values to be assigned to variables.
AntsBased Routing in Large Scale Mobile AdHoc Networks
 In Kommunikation in verteilten Systemen (KiVS03
, 2003
"... In this paper, we address the problem of routing in largescale mobile adhoc networks (MANETs), both in terms of number of nodes and coverage area. Our approach aims at abstracting from the dynamic, irregular topology of a MANET to obtain a topology with "logical routers" and "log ..."
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Cited by 27 (0 self)
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In this paper, we address the problem of routing in largescale mobile adhoc networks (MANETs), both in terms of number of nodes and coverage area. Our approach aims at abstracting from the dynamic, irregular topology of a MANET to obtain a topology with "logical routers" and "logical links", where logical router and logical links are just a collection of nodes and (multihop) paths between them, respectively. To "build" these logical routers, nodes geographically close to each other are grouped together. Logical links are established between selected logical routers. On top of this abstract topology, we propose to run a routing protocol based on mobile agents and inspired from social insects behaviour.
On the runtime analysis of the 1ANT ACO algorithm
 IN GENETIC AND EVOLUTIONARY COMPUTATION CONFERENCE, GECCO 2007, PROCEEDINGS
, 2007
"... The runtime analysis of randomized search heuristics is a growing field where, in the last two decades, many rigorous results have been obtained. These results, however, apply particularly to classical search heuristics such as Evolutionary Algorithms (EAs) and Simulated Annealing. First runtime ana ..."
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Cited by 17 (11 self)
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The runtime analysis of randomized search heuristics is a growing field where, in the last two decades, many rigorous results have been obtained. These results, however, apply particularly to classical search heuristics such as Evolutionary Algorithms (EAs) and Simulated Annealing. First runtime analyses of modern search heuristics have been conducted only recently w. r. t. a simple Ant Colony Optimization (ACO) algorithm called 1ANT. In particular, the influence of the evaporation factor in the pheromone update mechanism and the robustness of this parameter w. r. t. the runtime behavior have been determined for the example function OneMax. This paper puts forward the rigorous runtime analysis of the 1ANT on example functions, namely on the functions LeadingOnes and BinVal. With respect to EAs, such analyses have been essential to develop methods for the analysis on more complicated problems. The proof techniques required for the 1ANT, unfortunately, differ significantly from those for EAs, which means that a new reservoir of methods has to be built up. Again, the influence of the evaporation factor is analyzed rigorously, and it is proved that its choice can be very crucial to allow efficient runtimes. Moreover, the analyses provide insight into the working principles of ACO algorithms and, in terms of their robustness, describe essential differences to other randomized search heuristics.
A Study of Greedy, Local Search and Ant Colony Optimization Approaches for Car Sequencing Problems
 In Applications of evolutionary computing, volume 2611 of LNCS
, 2003
"... This paper describes and compares several heuristic approaches for the car sequencing problem. We rst study greedy heuristics, and show that dynamic ones clearly outperform their static counterparts. ..."
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Cited by 17 (2 self)
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This paper describes and compares several heuristic approaches for the car sequencing problem. We rst study greedy heuristics, and show that dynamic ones clearly outperform their static counterparts.
Ant Colony Optimisation and Local Search for Bin Packing and Cutting Stock Problems
 Journal of the Operational Research Society. (forthcoming
, 2003
"... The Bin Packing Problem and the Cutting Stock Problem are two related classes of NPhard combinatorial optimisation problems. Exact solution methods can only be used for very small instances, so for realworld problems we have to rely on heuristic methods. In recent years, researchers have started t ..."
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Cited by 16 (1 self)
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The Bin Packing Problem and the Cutting Stock Problem are two related classes of NPhard combinatorial optimisation problems. Exact solution methods can only be used for very small instances, so for realworld problems we have to rely on heuristic methods. In recent years, researchers have started to apply evolutionary approaches to these problems, including Genetic Algorithms and Evolutionary Programming. In the work presented here, we used an ant colony optimisation (ACO) approach to solve both Bin Packing and Cutting Stock Problems. We present a pure ACO approach, as well as an ACO approach augmented with a simple but very effective local search algorithm. It is shown that the pure ACO approach can outperform some existing solution methods, whereas the hybrid approach can compete with the best known solution methods. The local search algorithm is also run with random restarts and shown to perform significantly worse than when combined with ACO.
Ant Colony Optimization
 Optimization Techniques in Engineering. SpringerVerlag
, 2004
"... Introduction Ant Colony Optimization (ACO) is a paradigm for designing metaheuristic algorithms for combinatorial optimization problems. The first algorithm which can be classified within this framework was presented in 1991 [21 , 13] and, since then, many diverse variants of the basic principle ha ..."
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Cited by 12 (0 self)
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Introduction Ant Colony Optimization (ACO) is a paradigm for designing metaheuristic algorithms for combinatorial optimization problems. The first algorithm which can be classified within this framework was presented in 1991 [21 , 13] and, since then, many diverse variants of the basic principle have been reported in the literature. The essential trait of ACO algorithms is the combination of a priori information about the structure of a promising solution with a posteriori information about the structure of previously obtained good solutions. Metaheuristic algorithms are algorithms which, in order to escape from local optima, drive some basic heuristic: either a constructive heuristic starting from a null solution and adding elements to build a good complete one, or a local search heuristic starting from a complete solution and iteratively modifying some of its elements in order to achieve a better one. The metaheuristic part permits the lowlevel heuristic to obtain solutions better