An Ant Colony Approach to the Orienteering Problem

Liang, Yun-Chia; Smith, Alice E.

doi:10.1080/10170660609509336

Cited by 23 publications

(12 citation statements)

References 47 publications

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“…All these heuristics were tested using the small size benchmark problems provided in [70] with improving solution quality. More recently, many types of sophisticated meta-heuristic techniques have been applied on the OP, for example: artificial neural networks [72], tabu search [33], genetic algorithms [67], and ant colony optimization [52]. These meta-heuristics can provide comparable results on much larger scale problems (up to 300 nodes); among them, the tabu search has been shown to provide an optimality gap that is less than 1% in a number of experiments.…”

Section: Routing Problem With Profitsmentioning

confidence: 98%

A two‐stage vehicle routing model for large‐scale bioterrorism emergencies

2009

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In this article, we are interested in routing vehicles to service a large-scale bioterrorism emergency. We describe the specifics of routing vehicles in such a large-scale emergency and decompose the problem into two stages: a planning stage and an operational stage. In the planning stage, we generate the routes well in advance of any emergency. In the operational stage, we take into account the planned routes and the information revealed at the time of the emergency, to decide the delivery quantity and any adjustments to the routes. We propose mathematical formulations and solution approaches for both stages. Lastly, we demonstrate the effectiveness of our formulations and solution procedures in developing robust routes through numerical experiments.

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Section: Routing Problem With Profitsmentioning

confidence: 98%

A two‐stage vehicle routing model for large‐scale bioterrorism emergencies

2009

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“…However, methods that use ant colonies to solve the OP have been demonstrated [22] to obtain suboptimal solutions with better response times.…”

Section: Orienteering Problem Overviewmentioning

confidence: 99%

“…The selection of the next node to visit is carried out either by randomly exploring the search space or by applying a probabilistic state transition rule that takes into account the attractiveness of the nodes and the pheromone trails that have been deposited by other artificial ants (see [22] for a mathematical definition of this process). Once m solutions have been obtained, the best tour is selected, pheromone trails are intensified for the graph edges in the winning tour and evaporation is applied to the remaining trails in the graph.…”

Section: Ant Colony Optimization Overviewmentioning

confidence: 99%

A multicriteria ant colony algorithm for generating music playlists

Mocholi

Martínez

Jaén

et al. 2012

Expert Systems with Applications

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ElsevierMocholi Agües, JA.; Martinez Valero, VM.; Jaén Martínez, FJ.; Catalá Bolós, A. (2012) es ABSTRACTIn this paper we address the problem of music playlist generation based on the user-personalized specification of context information. We propose a generic semantic multicriteria ant colony algorithm capable of dealing with domain-specific problems by the use of ontologies. It also employs any associated metadata defined in the search space to feed its solution-building process and considers any restrictions the user may have specified. An example is given of the use of the algorithm for the problem of automatic generation of music playlists, some experimental results are presented and the behavior of the approach is explained in different situations.

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“…Apply ACO-OP algorithm END SACO SACO is different from other ACO algorithms, such as ACO-OP [20], in that the former uses semantic descriptions of concepts to dynamically assign scores to nodes representing problem domain entities in a process called semantic score assignment. In addition, this process is not performed statically and only once for every user according to his profile, but instead is a dynamic process that considers the restrictions defined either automatically or by the user.…”

Section: Saco: An Ontology-based Multicriteria Aco Algorithmmentioning

confidence: 99%

“…The selection of the next node to visit is carried out either by randomly exploring the search space or by applying a probabilistic state transition rule that takes into account the attractiveness of the nodes and the pheromone trails that have been deposited by other artificial ants (see [20] for a mathematical definition of this process). Once m solutions have been obtained, the best tour is selected, pheromone trails are intensified for the graph edges in the winning tour and evaporation is applied to the remaining trails in the graph.…”

Section: Ant Colony Optimization Overviewmentioning

confidence: 99%

Learning semantically-annotated routes for context-aware recommendations on map navigation systems

Mocholi

Jaén

Krynicki

et al. 2012

Applied Soft Computing

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ElsevierMocholi Agües, JA.; Jaén Martínez, FJ.; Krynicki, KK.; Catalá Bolós, A.;Picón, A.;Cadenas, A. (2012) ABSTRACTModern technology has brought many changes to our everyday lives. Our need to be in constant touch with others has been met with the cellphone, which has become our companion and the convergence point of many technological advances. The combination of capabilities such as browsing the Internet and GPS reception has multiplied the services and applications based on the current location of the user.However, providing the user with these services has certain drawbacks. Although map navigation systems are the most meaningful way of displaying this information, the user still has to manually set up the filter in order to obtain a non-bloated visualization of the map and the available services. To tackle this problem, we present here a semantic multicriteria ant colony algorithm capable of learning the user's routes, including associated context information, and then predicting the most likely route a user is following, given his current location and context data. This knowledge could then be used as the basis for offering services related to his current (or most likely future) context data close to the path he is following. Our experimental results show that our algorithm is capable of obtaining consistent solutions sets even when multiple objective ontological terms are included in the process.

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An Ant Colony Approach to the Orienteering Problem

Cited by 23 publications

References 47 publications

A two‐stage vehicle routing model for large‐scale bioterrorism emergencies

A two‐stage vehicle routing model for large‐scale bioterrorism emergencies

A multicriteria ant colony algorithm for generating music playlists

Learning semantically-annotated routes for context-aware recommendations on map navigation systems

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