A two-step evolutionary and ACO approach for solving the multi-agent patrolling problem

Lauri, Fabrice; Koukam, Abderraf́ıâa

doi:10.1109/cec.2008.4630897

Cited by 16 publications

(18 citation statements)

References 7 publications

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“…The strategies belonging to the first class may be computed by an algorithm based on the Lin-Kernighan heuristic [10], as pointed out by Chevaleyre [3]. The strategies of the second class may be computed by an hybrid algorithm based on a combination of an Evolutionary Algorithm (EA) and an AntColony Optimization (ACO) algorithm [9]. We propose singlecore and multi-core variants for solving this problem.…”

Section: Introductionmentioning

confidence: 99%

Hybrid ACO/EA algorithms applied to the multi-agent patrolling problem

Lauri¹,

Koukam²

2014

2014 IEEE Congress on Evolutionary Computation (CEC)

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Patrolling an environment consists in visiting as frequently as possible its most relevant areas in order to supervise, control or protect it. This task is commonly performed by a team of agents that need to coordinate their actions for achieving optimal performance. We address here the problem of multiagent patrolling in known environments where agents may move at different speeds and visit priorities on some areas may be specified. Two classes of patrolling strategies are studied: the single-cycle strategies and the partition-based strategies. Several single-core and multi-core variants of a template state-of-theart hybrid algorithm are proposed for generating partitionbased strategies. These are experimentally compared with a state-of-the-art heuristic-based algorithm generating single-cycle strategies. Experimental results show that: the heuristic-based algorithm only generates efficient strategies when agents move at the same speeds and no visit priorities have been defined; all single-core variants are equivalent; multi-core hybrid algorithms may improve overall quality or reduce variance of the solutions obtained by single-core algorithms.

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Section: Introductionmentioning

confidence: 99%

Hybrid ACO/EA algorithms applied to the multi-agent patrolling problem

Lauri¹,

Koukam²

2014

2014 IEEE Congress on Evolutionary Computation (CEC)

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“…The information that the ants collect during the search process is stored in the pheromone trail  with edges. The ant cooperates in finding the solution by exchanging information by the help of the pheromone chemical [9]. Edges are also associated with heuristic value  which represents information about the problem instance or its value is inverse of the distance.…”

Section: Acomentioning

confidence: 99%

Ant Colony Based Path Planning Algorithm for Autonomous Robotic Vehicles

Gigras¹

2012

IJAIA

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“…The patrolling problem is usually specified formally as in [11,2,9,8]. Let the graph G = (V, E) where V is the set of nodes and E the edges.…”

Section: Mathematical Framework Of the Patrolling Taskmentioning

confidence: 99%

“…Such a strategy must optimize one or several given quality criteria [9]. π = {π 1 · · · π p } is made up of the p individual strategies π i of each agent i.…”

Section: Mathematical Framework Of the Patrolling Taskmentioning

confidence: 99%

Modelling and Self-organizing in Mobile Wireless Sensor Networks: Application to Fire Detection

TagneFute¹,

Tonyé²

2013

IJAIS

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This paper describes the design of a system for wildfire monitoring incorporating wireless sensors. A wireless sensor network (WSN) is a complex system. In complex systems, multiple aspects interact and influence each other. A vast number of entities are present in the system. Traditional modelling and simulation techniques fail to capture interactions between loosely coupled aspects of a complex system. This paper also describes a modelling approach of the mobile WSN based on Holonic MultiAgent paradigm (Holonic Multi-Agent Systems (HMAS)). The holonic model has the characteristics of scalability and robustness. The approach of modelling used is organizational and it uses four basic concepts: Role, Interaction, Organization and Capacity. This paper mainly presents the formulation problem of fire detection. An application is made to the patrolling task and the detection and avoidance of the obstacles during moving of the mobile sensor.

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A two-step evolutionary and ACO approach for solving the multi-agent patrolling problem

Cited by 16 publications

References 7 publications

Hybrid ACO/EA algorithms applied to the multi-agent patrolling problem

Hybrid ACO/EA algorithms applied to the multi-agent patrolling problem

Ant Colony Based Path Planning Algorithm for Autonomous Robotic Vehicles

Modelling and Self-organizing in Mobile Wireless Sensor Networks: Application to Fire Detection

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