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411
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 48 (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 for continuous domains
, 2008
"... In this paper we present an extension of ant colony optimization (ACO) to continuous domains. We show how ACO, which was initially developed to be a metaheuristic for combinatorial optimization, can be adapted to continuous optimization without any major conceptual change to its structure. We presen ..."
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Cited by 41 (5 self)
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In this paper we present an extension of ant colony optimization (ACO) to continuous domains. We show how ACO, which was initially developed to be a metaheuristic for combinatorial optimization, can be adapted to continuous optimization without any major conceptual change to its structure. We present the general idea, implementation, and results obtained. We compare the results with those reported in the literature for other continuous optimization methods: other antrelated approaches and other metaheuristics initially developed for combinatorial optimization and later adapted to handle the continuous case. We discuss how our extended ACO compares to those algorithms, and we present some analysis of its efficiency and robustness.
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 41 (8 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.
Ant Colony Optimization  Artificial Ants as a Computational Intelligence Technique
 IEEE COMPUT. INTELL. MAG
, 2006
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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 36 (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.
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 35 (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.
An Ant Colony Optimization Approach for the Single Machine Total Tardiness Problem
 In CEC99: Proceedings of the Congress on Evolutionary Computation
, 1999
"... Machine scheduling is a central task in production planning. In general it means the problem of scheduling job operations on a given number of available machines. In this paper we consider a machine scheduling problem with one machine, the Single Machine Total Tardiness Problem. To solve this NPhar ..."
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Cited by 35 (0 self)
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Machine scheduling is a central task in production planning. In general it means the problem of scheduling job operations on a given number of available machines. In this paper we consider a machine scheduling problem with one machine, the Single Machine Total Tardiness Problem. To solve this NPhard problem, we apply the ant colony optimization metaphor, a recently developed metaheuristic that has proven its potential for various other combinatorial optimization problems. We test our algorithm using 125 benchmark problems and present computational results. 1 Introduction Ant Colony Optimization (ACO) is a rather new metaheuristic introduced in the early nineties (cf. [6, 7, 11, 15]) and has successfully been applied to several combinatorial optimization problems (cf. e.g. [4, 5, 8, 9, 16, 21, 25]). In this paper we apply ACO to the Single Machine Total Tardiness Problem y We would like to thank Herbert Dawid and Marco Dorigo for their contributions to this research. Financial sup...
Choosing Search Heuristics by NonStationary Reinforcement Learning
"... Search decisions are often made using heuristic methods because realworld applications can rarely be tackled without any heuristics. In many cases, multiple heuristics can potentially be chosen, and it is not clear a priori which would perform best. In this article, we propose a procedure that learn ..."
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Cited by 32 (1 self)
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Search decisions are often made using heuristic methods because realworld applications can rarely be tackled without any heuristics. In many cases, multiple heuristics can potentially be chosen, and it is not clear a priori which would perform best. In this article, we propose a procedure that learns, during the search process, how to select promising heuristics. The learning is based on weight adaptation and can even switch between di#erent heuristics during search. Di#erent variants of the approach are evaluated within a constraintprogramming environment.
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 31 (9 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.
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 31 (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