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474
Modelbased search for combinatorial optimization
, 2001
"... Abstract In this paper we introduce modelbased search as a unifying framework accommodating some recently proposed heuristics for combinatorial optimization such as ant colony optimization, stochastic gradient ascent, crossentropy and estimation of distribution methods. We discuss similarities as ..."
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Cited by 60 (13 self)
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Abstract In this paper we introduce modelbased search as a unifying framework accommodating some recently proposed heuristics for combinatorial optimization such as ant colony optimization, stochastic gradient ascent, crossentropy and estimation of distribution methods. We discuss similarities as well as distinctive features of each method, propose some extensions and present a comparative experimental study of these algorithms. 1
An Ant Colony System Hybridized With A New Local Search For The Sequential Ordering Problem
, 2000
"... We present a new local optimizer called SOP3exchange for the sequential ordering problem that extends a local search for the traveling salesman problem to handle multiple constraints directly without increasing computational complexity. An algorithm that combines the SOP3exchange with an Ant Col ..."
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Cited by 59 (13 self)
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We present a new local optimizer called SOP3exchange for the sequential ordering problem that extends a local search for the traveling salesman problem to handle multiple constraints directly without increasing computational complexity. An algorithm that combines the SOP3exchange with an Ant Colony Optimization algorithm is described and we present experimental evidence that the resulting algorithm is more effective than existing methods for the problem. The bestknown results for many of a standard test set of 22 problems are improved using the SOP3exchange with our Ant Colony Optimization algorithm or in combination with the MPO/AI algorithm (Chen and Smith 1996).
Ant Colony Optimization  Artificial Ants as a Computational Intelligence Technique
 IEEE COMPUT. INTELL. MAG
, 2006
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Stupid Robot Tricks: A BehaviorBased Distributed Algorithm Library for Programming Swarms of Robots
, 2004
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Cited by 53 (8 self)
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A hierarchical particle swarm optimizer and its adaptive variant
 Systems, Man, and Cybernetics, Part B: Cybernetics, IEEE Transactions on
, 2005
"... Abstract—A hierarchical version of the particle swarm optimization (PSO) metaheuristic is introduced in this paper. In the new method called HPSO, the particles are arranged in a dynamic hierarchy that is used to define a neighborhood structure. Depending on the quality of their sofar bestfound ..."
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Cited by 51 (0 self)
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Abstract—A hierarchical version of the particle swarm optimization (PSO) metaheuristic is introduced in this paper. In the new method called HPSO, the particles are arranged in a dynamic hierarchy that is used to define a neighborhood structure. Depending on the quality of their sofar bestfound solution, the particles move up or down the hierarchy. This gives good particles that move up in the hierarchy a larger influence on the swarm. We introduce a variant of HPSO, in which the shape of the hierarchy is dynamically adapted during the execution of the algorithm. Another variant is to assign different behavior to the individual particles with respect to their level in the hierarchy. HPSO and its variants are tested on a commonly used set of optimization functions and are compared to PSO using different standard neighborhood schemes.
Better Group Behaviors in Complex Environments using Global Roadmaps
 In Artif. Life
, 2002
"... While many methods to simulate flocking behaviors have been proposed, these techniques usually only provide simplistic navigation and planning capabilities because each flock member's behavior depends only on its local environment. In this work, we investigate how the addition of global in ..."
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Cited by 51 (12 self)
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While many methods to simulate flocking behaviors have been proposed, these techniques usually only provide simplistic navigation and planning capabilities because each flock member's behavior depends only on its local environment. In this work, we investigate how the addition of global information in the form of a roadmap of the environment enables more sophisticated flocking behaviors and supports global navigation and planning.
A MAXMIN Ant System for the University Course Timetabling Problem
 in Proceedings of the 3rd International Workshop on Ant Algorithm, ANTS 2002, Lecture Notes in Computer Science
, 2002
"... We consider a simplification of a typical university course timetabling problem involving three types of hard and three types of soft constraints. A MAXMIN Ant System, which makes use of a separate local search routine, is proposed for tackling this problem. We devise an appropriate construction gr ..."
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Cited by 50 (0 self)
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We consider a simplification of a typical university course timetabling problem involving three types of hard and three types of soft constraints. A MAXMIN Ant System, which makes use of a separate local search routine, is proposed for tackling this problem. We devise an appropriate construction graph and pheromone matrix representation after considering alternatives. The resulting algorithm is tested over a set of eleven instances from three classes of the problem. The results demonstrate that the ant system is able to construct significantly better timetables than an algorithm that iterates the local search procedure from random starting solutions.
RoadmapBased Flocking for Complex Environments
 Proc. 10th Pacific Conference on Computer Graphics and Applications (PG’02
, 2004
"... Flocking behavior is very common in nature, and there have been ongoing research efforts to simulate such behavior in computer animations and robotics applications. Generally, such work considers behaviors that can be determined independently by each flock member solely by observing its local enviro ..."
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Cited by 45 (14 self)
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Flocking behavior is very common in nature, and there have been ongoing research efforts to simulate such behavior in computer animations and robotics applications. Generally, such work considers behaviors that can be determined independently by each flock member solely by observing its local environment, e.g., the speed and direction of its neighboring flock members. Since flock members are not assumed to have global information about the environment, only very simple navigation and planning techniques have been considered for such flocks.
Using ANT Agents to combine reactive and proactive strategies for routing in mobile adhoc networks
"... This paper describes AntHocNet, an algorithm for routing in mobile ad hoc networks based on ideas from the Ant Colony Optimization framework. In AntHocNet a source node reactively sets up a path to a destination node at the start of each communication session. During the course of the session, the s ..."
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Cited by 44 (16 self)
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This paper describes AntHocNet, an algorithm for routing in mobile ad hoc networks based on ideas from the Ant Colony Optimization framework. In AntHocNet a source node reactively sets up a path to a destination node at the start of each communication session. During the course of the session, the source node uses ant agents to proactively search for alternatives and improvements of the original path. This allows to adapt to changes in the network, and to construct a mesh of alternative paths between source and destination. The proactive behavior is supported by a lightweight information bootstrapping process. Paths are represented in the form of distancevector routing tables called pheromone tables. An entry of a pheromone table contains the estimated goodness of going over a certain neighbor to reach a certain destination. Data are routed stochastically over the different paths of the mesh according to these goodness estimates. In an extensive set of simulation tests, we compare AntHocNet to AODV, a reactive algorithm which is an important reference in this research area. We show that AntHocNet can outperform AODV for different evaluation criteria in a wide range of different scenarios. AntHocNet is also shown to scale well with respect to the number of nodes.
Ant colony optimization for the total weighted tardiness problem
 In Proceedings of the Parallel Problem Solving from Nature Conference
, 2000
"... Abstract. In this article we present an application of the Ant Colony Optimization (ACO) metaheuristic to the single machine total weighted tardiness problem. First, we briefly discuss the constructive phase of ACO in which a colony of artificial ants generates a set of feasible solutions. Then, we ..."
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Cited by 42 (5 self)
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Abstract. In this article we present an application of the Ant Colony Optimization (ACO) metaheuristic to the single machine total weighted tardiness problem. First, we briefly discuss the constructive phase of ACO in which a colony of artificial ants generates a set of feasible solutions. Then, we introduce some simple but very effective local search. Last, we combine the constructive phase with local search obtaining a novel ACO algorithm that uses a heterogeneous colony of ants and is highly effective in finding the bestknown solutions on all instances of a widely used set of benchmark problems. 1