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2017 |
Ramirez-Atencia, Cristian; R-Moreno, Maria D; Camacho, David Handling swarm of UAVs based on evolutionary multi-objective optimization Journal Article Progress in Artificial Intelligence, In Press , pp. 1–12, 2017, ISSN: 2192-6352. Abstract | Links | BibTeX | Tags: Constraint Satisfaction Problems, Mission Planning, Multi-Objective Genetic Algorithm, Unmanned Aerial Vehicles @article{Ramirez-Atencia2017, title = {Handling swarm of UAVs based on evolutionary multi-objective optimization}, author = {Cristian Ramirez-Atencia and Maria D R-Moreno and David Camacho}, url = {http://link.springer.com/10.1007/s13748-017-0123-7}, doi = {10.1007/s13748-017-0123-7}, issn = {2192-6352}, year = {2017}, date = {2017-01-01}, journal = {Progress in Artificial Intelligence}, volume = {In Press}, pages = {1--12}, publisher = {Springer Berlin Heidelberg}, abstract = {The fast technological improvements in unmanned aerial vehicles (UAVs) has created new scenarios where a swarm of UAVs could operate in a distributed way. This swarm of vehicles needs to be controlled from a set of ground control stations, and new reliable mission planning systems, which should be able to handle the large amount of variables and constraints. This paper presents a new approach where this complex problem has been modelled as a constraint satisfaction problem (CSP), and is solved using a multi-objective genetic algorithm (MOGA). The algorithm has been designed to minimize several variables of the mission, such as the fuel consumption or the makespan among others. The designed fitness function, used by the algorithm, takes into consideration, as a weighted penalty function, the number of constraints fulfilled for each solution. Therefore, the MOGA algorithm is able to manage the number of constraints fulfilled by the selected plan, so it is possible to maximize in the elitism phase of the MOGA the quality of the solutions found. This approach allows to alleviate the computational effort carried out by the CSP solver, finding new solutions from the Pareto front, and therefore reducing the execution time to obtain a solution. In order to test the performance of this new approach 16 different mission scenarios have been designed. The experimental results show that the approach outperforms the convergence of the algorithm in terms of number of generations and runtime.}, keywords = {Constraint Satisfaction Problems, Mission Planning, Multi-Objective Genetic Algorithm, Unmanned Aerial Vehicles}, pubstate = {published}, tppubtype = {article} } The fast technological improvements in unmanned aerial vehicles (UAVs) has created new scenarios where a swarm of UAVs could operate in a distributed way. This swarm of vehicles needs to be controlled from a set of ground control stations, and new reliable mission planning systems, which should be able to handle the large amount of variables and constraints. This paper presents a new approach where this complex problem has been modelled as a constraint satisfaction problem (CSP), and is solved using a multi-objective genetic algorithm (MOGA). The algorithm has been designed to minimize several variables of the mission, such as the fuel consumption or the makespan among others. The designed fitness function, used by the algorithm, takes into consideration, as a weighted penalty function, the number of constraints fulfilled for each solution. Therefore, the MOGA algorithm is able to manage the number of constraints fulfilled by the selected plan, so it is possible to maximize in the elitism phase of the MOGA the quality of the solutions found. This approach allows to alleviate the computational effort carried out by the CSP solver, finding new solutions from the Pareto front, and therefore reducing the execution time to obtain a solution. In order to test the performance of this new approach 16 different mission scenarios have been designed. The experimental results show that the approach outperforms the convergence of the algorithm in terms of number of generations and runtime. |
Ramirez-Atencia, Cristian; Mostaghim, Sanaz; Camacho, David A Knee Point Based Evolutionary Multi-objective Optimization for Mission Planning Problems Inproceedings Proceedings of the Genetic and Evolutionary Computation Conference, pp. 1216–1223, ACM, Berlin, Germany, 2017, ISBN: 978-1-4503-4920-8. Abstract | Links | BibTeX | Tags: Constraint Satisfaction Problems, evolutionary multi-objective optimization, knee point, Mission Planning, Multi-objective Optimization, UAVs @inproceedings{Ramirez-Atencia2017b, title = {A Knee Point Based Evolutionary Multi-objective Optimization for Mission Planning Problems}, author = {Cristian Ramirez-Atencia and Sanaz Mostaghim and David Camacho}, url = {http://doi.acm.org/10.1145/3071178.3071319}, doi = {10.1145/3071178.3071319}, isbn = {978-1-4503-4920-8}, year = {2017}, date = {2017-01-01}, booktitle = {Proceedings of the Genetic and Evolutionary Computation Conference}, pages = {1216--1223}, publisher = {ACM}, address = {Berlin, Germany}, series = {GECCO '17}, abstract = {The current boom of Unmanned Aerial Vehicles (UAVs) is increasing the number of potential industrial and research applications. One of the most demanded topics in this area is related to the automated planning of a UAVs swarm, controlled by one or several Ground Control Stations (GCSs). In this context, there are several variables that influence the selection of the most appropriate plan, such as the makespan, the cost or the risk of the mission. This problem can be seen as a Multi-Objective Optimization Problem (MOP). On previous approaches, the problem was modelled as a Constraint Satisfaction Problem (CSP) and solved using a Multi-Objective Genetic Algorithm (MOGA), so a Pareto Optimal Frontier (POF) was obtained. The main problem with this approach is based on the large number of obtained solutions, which hinders the selection of the best solution. This paper presents a new algorithm that has been designed to obtain the most significant solutions in the POF. This approach is based on Knee Points applied to MOGA. The new algorithm has been proved in a real scenario with different number of optimization variables, the experimental results show a significant improvement of the algorithm performance.}, keywords = {Constraint Satisfaction Problems, evolutionary multi-objective optimization, knee point, Mission Planning, Multi-objective Optimization, UAVs}, pubstate = {published}, tppubtype = {inproceedings} } The current boom of Unmanned Aerial Vehicles (UAVs) is increasing the number of potential industrial and research applications. One of the most demanded topics in this area is related to the automated planning of a UAVs swarm, controlled by one or several Ground Control Stations (GCSs). In this context, there are several variables that influence the selection of the most appropriate plan, such as the makespan, the cost or the risk of the mission. This problem can be seen as a Multi-Objective Optimization Problem (MOP). On previous approaches, the problem was modelled as a Constraint Satisfaction Problem (CSP) and solved using a Multi-Objective Genetic Algorithm (MOGA), so a Pareto Optimal Frontier (POF) was obtained. The main problem with this approach is based on the large number of obtained solutions, which hinders the selection of the best solution. This paper presents a new algorithm that has been designed to obtain the most significant solutions in the POF. This approach is based on Knee Points applied to MOGA. The new algorithm has been proved in a real scenario with different number of optimization variables, the experimental results show a significant improvement of the algorithm performance. |
2016 |
Ramirez-Atencia, Cristian; Bello-Orgaz, Gema; R-Moreno, Maria D; Camacho, David A Weighted Penalty Fitness for a Hybrid MOGA-CSP to solve Mission Planning Problems Inproceedings XI Congreso Español de Metaheurísticas, Algoritmos Evolutivos y Bioinspirados (MAEB 2016), pp. 305–314, 2016. Abstract | Links | BibTeX | Tags: Constraint Satisfaction Problems, Mission Planning, Muli-UAV, Multi-Objective Genetic Algorithm, Multi-objective Optimization, NSGA2, Unmanned Aerial Vehicles @inproceedings{Ramirez-Atencia2016a, title = {A Weighted Penalty Fitness for a Hybrid MOGA-CSP to solve Mission Planning Problems}, author = {Cristian Ramirez-Atencia and Gema Bello-Orgaz and Maria D R-Moreno and David Camacho}, url = {http://aida.ii.uam.es/wp-content/uploads/2017/03/A-Weighted-Penalty-Fitness-for-a-Hybrid-MOGA-CSP-to-solve-Mission-Planning-Problems.pdf}, year = {2016}, date = {2016-01-01}, booktitle = {XI Congreso Español de Metaheurísticas, Algoritmos Evolutivos y Bioinspirados (MAEB 2016)}, pages = {305--314}, abstract = {Unmanned Aerial Vehicles (UAVs) are currently booming due to their high number of potential applications. In Mission Planning problems, several tasks must be performed by a team of UAVs, under the supervision of one or more Ground Control Stations (GCSs). In our approach, we have modelled the problem as a Constraint Satisfaction Problem (CSP), and solved it using a Multi-Objective Genetic Algorithm (MOGA). The algorithm has been designed to minimize several variables of the mission such as the fuel consumption or the makespan. In addition, the fitness function takes a new consideration when solutions are not valid. It uses the number of constraints fulfilled for each solution as a weighted penalty function. In this way, the number of constraints fulfilled is maximized in the elitism phase of the MOGA. Results show that the approach outperforms the convergence with respect to previous results.}, keywords = {Constraint Satisfaction Problems, Mission Planning, Muli-UAV, Multi-Objective Genetic Algorithm, Multi-objective Optimization, NSGA2, Unmanned Aerial Vehicles}, pubstate = {published}, tppubtype = {inproceedings} } Unmanned Aerial Vehicles (UAVs) are currently booming due to their high number of potential applications. In Mission Planning problems, several tasks must be performed by a team of UAVs, under the supervision of one or more Ground Control Stations (GCSs). In our approach, we have modelled the problem as a Constraint Satisfaction Problem (CSP), and solved it using a Multi-Objective Genetic Algorithm (MOGA). The algorithm has been designed to minimize several variables of the mission such as the fuel consumption or the makespan. In addition, the fitness function takes a new consideration when solutions are not valid. It uses the number of constraints fulfilled for each solution as a weighted penalty function. In this way, the number of constraints fulfilled is maximized in the elitism phase of the MOGA. Results show that the approach outperforms the convergence with respect to previous results. |
Ramirez-Atencia, Cristian; Bello-Orgaz, Gema; R-Moreno, Maria D; Camacho, David MOGAMR: A Multi-Objective Genetic Algorithm for Real-Time Mission Replanning Inproceedings 2016 IEEE Symposium Series on Computational Intelligence (SSCI), 2016, ISBN: 978-1-5090-4240-1, 978-1-5090-4241-8. Abstract | Links | BibTeX | Tags: Constraint Satisfaction Problems, Metaheuristics, Mission Planning, Multi-objective Optimization, NSGA2, Replanning, Unmanned Aircraft Systems @inproceedings{Ramirez-Atencia2016b, title = {MOGAMR: A Multi-Objective Genetic Algorithm for Real-Time Mission Replanning}, author = {Cristian Ramirez-Atencia and Gema Bello-Orgaz and Maria D R-Moreno and David Camacho}, doi = {10.1109/SSCI.2016.7850235}, isbn = {978-1-5090-4240-1, 978-1-5090-4241-8}, year = {2016}, date = {2016-01-01}, booktitle = {2016 IEEE Symposium Series on Computational Intelligence (SSCI)}, abstract = {From the last few years the interest and repercussion on Unmanned Aerial Vehicle (UAV) technologies have been extended from pure military applications to industrial and societal applications. One of the basic tasks to any UAV problems is related to the Mission Planning. This problem is particularly complex when a set of UAVs is considered. In the field of MultiUAV Mission Planning, some approaches have been carried out in the last years. However, there are few works related to realtime Mission Replanning, which is the focus of this work. In Mission Replanning, some changes in the mission, such as the arrival of new tasks, require to update the preplanned solution as fast as possible. In this paper a Multi-Objective Genetic Algorithm for Mission Replanning (MOGAMR) is proposed to handle this problem. This approach uses a set of previous plans (or solutions), generated using an offlline planning process, in order to initialize the population of the algorithm, then acts as a complete regeneration method. In order to simulate a real-time system we have fixed a time limit of 2 minutes. This has been considered as an appropriate time for a human operator to take a decision. Using this time restriction, a set of experiments adding from 1 to 5 new tasks in the Replanning Problems has been carried out. The experiments show that the algorithm works well with this few number of new tasks during the replanning process generating a set of feasible solutions under the time restriction considered.}, keywords = {Constraint Satisfaction Problems, Metaheuristics, Mission Planning, Multi-objective Optimization, NSGA2, Replanning, Unmanned Aircraft Systems}, pubstate = {published}, tppubtype = {inproceedings} } From the last few years the interest and repercussion on Unmanned Aerial Vehicle (UAV) technologies have been extended from pure military applications to industrial and societal applications. One of the basic tasks to any UAV problems is related to the Mission Planning. This problem is particularly complex when a set of UAVs is considered. In the field of MultiUAV Mission Planning, some approaches have been carried out in the last years. However, there are few works related to realtime Mission Replanning, which is the focus of this work. In Mission Replanning, some changes in the mission, such as the arrival of new tasks, require to update the preplanned solution as fast as possible. In this paper a Multi-Objective Genetic Algorithm for Mission Replanning (MOGAMR) is proposed to handle this problem. This approach uses a set of previous plans (or solutions), generated using an offlline planning process, in order to initialize the population of the algorithm, then acts as a complete regeneration method. In order to simulate a real-time system we have fixed a time limit of 2 minutes. This has been considered as an appropriate time for a human operator to take a decision. Using this time restriction, a set of experiments adding from 1 to 5 new tasks in the Replanning Problems has been carried out. The experiments show that the algorithm works well with this few number of new tasks during the replanning process generating a set of feasible solutions under the time restriction considered. |
Ramirez-Atencia, Cristian; Bello-Orgaz, Gema; R-Moreno, Maria D; Camacho, David Solving complex multi-UAV mission planning problems using multi-objective genetic algorithms Journal Article Soft Computing, In Press , pp. 1–18, 2016, ISSN: 1432-7643; 1433-7479. Abstract | Links | BibTeX | Tags: Constraint Satisfaction Problems, Genetic Algorithms, Mission Planning, Multi-objective Optimization, NSGA2, Unmanned air vehicles @article{Ramirez-Atencia2016c, title = {Solving complex multi-UAV mission planning problems using multi-objective genetic algorithms}, author = {Cristian Ramirez-Atencia and Gema Bello-Orgaz and Maria D R-Moreno and David Camacho}, url = {http://aida.ii.uam.es/wp-content/uploads/2017/03/RamirezEtAl.pdf}, doi = {10.1007/s00500-016-2376-7}, issn = {1432-7643; 1433-7479}, year = {2016}, date = {2016-01-01}, journal = {Soft Computing}, volume = {In Press}, pages = {1--18}, publisher = {Springer Verlag}, abstract = {Due to recent booming of unmanned air vehicles (UAVs) technologies, these are being used in many fields involving complex tasks. Some of them involve a high risk to the vehicle driver, such as fire monitoring and rescue tasks, which make UAVs excellent for avoiding human risks. Mission planning for UAVs is the process of planning the locations and actions (loading/dropping a load, taking videos/pictures, acquiring information) for the vehicles, typically over a time period. These vehicles are controlled from ground control stations (GCSs) where human operators use rudimentary systems. This paper presents a new multi-objective genetic algorithm for solving complex mission planning problems involving a team of UAVs and a set of GCSs. A hybrid fitness function has been designed using a constraint satisfaction problem to check whether solutions are valid and Pareto-based measures to look for optimal solutions. The algorithm has been tested on several datasets, optimizing different variables of the mission, such as the makespan, the fuel consumption, and distance. Experimental results show that the new algorithm is able to obtain good solutions; however, as the problem becomes more complex, the optimal solutions also become harder to find.}, keywords = {Constraint Satisfaction Problems, Genetic Algorithms, Mission Planning, Multi-objective Optimization, NSGA2, Unmanned air vehicles}, pubstate = {published}, tppubtype = {article} } Due to recent booming of unmanned air vehicles (UAVs) technologies, these are being used in many fields involving complex tasks. Some of them involve a high risk to the vehicle driver, such as fire monitoring and rescue tasks, which make UAVs excellent for avoiding human risks. Mission planning for UAVs is the process of planning the locations and actions (loading/dropping a load, taking videos/pictures, acquiring information) for the vehicles, typically over a time period. These vehicles are controlled from ground control stations (GCSs) where human operators use rudimentary systems. This paper presents a new multi-objective genetic algorithm for solving complex mission planning problems involving a team of UAVs and a set of GCSs. A hybrid fitness function has been designed using a constraint satisfaction problem to check whether solutions are valid and Pareto-based measures to look for optimal solutions. The algorithm has been tested on several datasets, optimizing different variables of the mission, such as the makespan, the fuel consumption, and distance. Experimental results show that the new algorithm is able to obtain good solutions; however, as the problem becomes more complex, the optimal solutions also become harder to find. |
2015 |
Ramirez-Atencia, Cristian; Bello-Orgaz, Gema; R-Moreno, María D; Camacho, David A Hybrid MOGA-CSP for Multi-UAV Mission Planning Inproceedings Proceedings of the Companion Publication of the 2015 on Genetic and Evolutionary Computation Conference, pp. 1205–1208, ACM 2015. Links | BibTeX | Tags: Constraint Satisfaction Problems, Genetic Algorithms, Mission Planning, Multi-objective Optimization, Unmanned Aircraft Systems @inproceedings{ramirez2015hybrid, title = {A Hybrid MOGA-CSP for Multi-UAV Mission Planning}, author = {Cristian Ramirez-Atencia and Gema Bello-Orgaz and María D R-Moreno and David Camacho}, url = {http://aida.ii.uam.es/wp-content/uploads/2015/09/ramirez-atenciaHybrid.pdf}, year = {2015}, date = {2015-01-01}, booktitle = {Proceedings of the Companion Publication of the 2015 on Genetic and Evolutionary Computation Conference}, pages = {1205--1208}, organization = {ACM}, keywords = {Constraint Satisfaction Problems, Genetic Algorithms, Mission Planning, Multi-objective Optimization, Unmanned Aircraft Systems}, pubstate = {published}, tppubtype = {inproceedings} } |
Ramírez-Atencia, Cristian; Orgaz, Gema Bello; Rodríguez-Moreno, María Dolores; Camacho, David Performance Evaluation of Multi-UAV Cooperative Mission Planning Models Inproceedings Computational Collective Intelligence - 7th International Conference, ICCCI 2015, Madrid, Spain, September 21-23, 2015, Proceedings, Part II, pp. 203–212, 2015. Abstract | Links | BibTeX | Tags: branch and bound, Constraint Satisfaction Problems, Mission Planning, Unmanned Aircraft Systems @inproceedings{DBLP:conf/iccci/Ramirez-Atencia15, title = {Performance Evaluation of Multi-UAV Cooperative Mission Planning Models}, author = {Cristian Ramírez-Atencia and Gema Bello Orgaz and María Dolores Rodríguez-Moreno and David Camacho}, url = {http://dx.doi.org/10.1007/978-3-319-24306-1_20 http://aida.ii.uam.es/wp-content/uploads/2015/09/ramirez-atenciaPerformance.pdf}, year = {2015}, date = {2015-01-01}, booktitle = {Computational Collective Intelligence - 7th International Conference, ICCCI 2015, Madrid, Spain, September 21-23, 2015, Proceedings, Part II}, pages = {203--212}, crossref = {DBLP:conf/iccci/2015-2}, abstract = {The Multi-UAV Cooperative Mission Planning Problem (MCMPP) is a complex problem which can be represented with a lower or higher level of complexity. In this paper we present a MCMPP which is modelled as a Constraint Satisfaction Problem (CSP) with 5 increasing levels of complexity. Each level adds additional variables and constraints to the problem. Using previous models, we solve the problem using a Branch and Bound search designed to minimize the fuel consumption and number of UAVs employed in the mission, and the results show how runtime increases as the level of complexity increases in most cases, as expected, but there are some cases where the opposite happens.}, keywords = {branch and bound, Constraint Satisfaction Problems, Mission Planning, Unmanned Aircraft Systems}, pubstate = {published}, tppubtype = {inproceedings} } The Multi-UAV Cooperative Mission Planning Problem (MCMPP) is a complex problem which can be represented with a lower or higher level of complexity. In this paper we present a MCMPP which is modelled as a Constraint Satisfaction Problem (CSP) with 5 increasing levels of complexity. Each level adds additional variables and constraints to the problem. Using previous models, we solve the problem using a Branch and Bound search designed to minimize the fuel consumption and number of UAVs employed in the mission, and the results show how runtime increases as the level of complexity increases in most cases, as expected, but there are some cases where the opposite happens. |
2014 |
Gonzalez-Pardo, Antonio; Camacho, David Solving Resource-Constraint Project Scheduling Problems based on ACO algorithms Conference Ninth International Conference on Swarm Intelligence (ANTS 2014)., 8667 , Lecture Notes in Computer Science of Springer-Verlag, 2014. Links | BibTeX | Tags: Ant Colony Optimization, Computational Intelligence, Constraint Satisfaction Problems, CSP-graph based representation @conference{2014-GonzalezCamachoANTS, title = {Solving Resource-Constraint Project Scheduling Problems based on ACO algorithms}, author = {Antonio Gonzalez-Pardo and David Camacho}, url = {http://aida.ii.uam.es/wp-content/uploads/2014/09/2014-ANTS-GonzalezCamacho.pdf}, year = {2014}, date = {2014-09-10}, booktitle = {Ninth International Conference on Swarm Intelligence (ANTS 2014).}, volume = {8667}, pages = {290-291}, publisher = {Lecture Notes in Computer Science of Springer-Verlag}, keywords = {Ant Colony Optimization, Computational Intelligence, Constraint Satisfaction Problems, CSP-graph based representation}, pubstate = {published}, tppubtype = {conference} } |
Gonzalez-Pardo, Antonio; Camacho, David A New CSP Graph-Based Representation to Resource-Constrained Project Scheduling Problem Conference 2014 IEEE Conference on Evolutionary Computation (CEC 2014), 2014. Links | BibTeX | Tags: Ant Colony Optimization, Computational Intelligence, Constraint Satisfaction Problems, CSP-graph based representation @conference{2014-GonzalezCamachoCEC, title = {A New CSP Graph-Based Representation to Resource-Constrained Project Scheduling Problem}, author = {Antonio Gonzalez-Pardo and David Camacho}, url = {http://aida.ii.uam.es/wp-content/uploads/2014/09/2014-CEC-GonzalezCamacho.pdf}, year = {2014}, date = {2014-07-07}, booktitle = {2014 IEEE Conference on Evolutionary Computation (CEC 2014)}, pages = {344-351}, keywords = {Ant Colony Optimization, Computational Intelligence, Constraint Satisfaction Problems, CSP-graph based representation}, pubstate = {published}, tppubtype = {conference} } |
Gonzalez-Pardo, Antonio; Palero, Fernando; Camacho, David Micro and Macro Lemmings simulations based on ants colonies Conference EvoApp 2014, In press , 2014. BibTeX | Tags: Ant Colony Optimization, Constraint Satisfaction Problems, CSP-graph based representation, Videogames @conference{14-GonzalezEtAl-EvoApp, title = {Micro and Macro Lemmings simulations based on ants colonies}, author = {Antonio Gonzalez-Pardo and Fernando Palero and David Camacho}, year = {2014}, date = {2014-04-23}, booktitle = {EvoApp 2014}, volume = {In press}, keywords = {Ant Colony Optimization, Constraint Satisfaction Problems, CSP-graph based representation, Videogames}, pubstate = {published}, tppubtype = {conference} } |
Gonzalez-Pardo, Antonio; Palero, Fernando; Camacho, David An empirical study on collective intelligence algorithms for video games problem-solving Journal Article Computing and Informatics, In press , 2014, ISSN: 1335-9150. BibTeX | Tags: Ant Colony Optimization, Constraint Satisfaction Problems, CSP-graph based representation, Videogames @article{14-GonzalezEtAl-CAI, title = {An empirical study on collective intelligence algorithms for video games problem-solving}, author = {Antonio Gonzalez-Pardo and Fernando Palero and David Camacho}, issn = {1335-9150}, year = {2014}, date = {2014-01-21}, journal = {Computing and Informatics}, volume = {In press}, keywords = {Ant Colony Optimization, Constraint Satisfaction Problems, CSP-graph based representation, Videogames}, pubstate = {published}, tppubtype = {article} } |
2013 |
Gonzalez-Pardo, Antonio; Camacho, David A new CSP graph-based representation for Ant Colony Optimization Conference 2013 IEEE Conference on Evolutionary Computation (CEC 2013), 1 , 2013. Links | BibTeX | Tags: Ant Colony Optimization, Computational Intelligence, Constraint Satisfaction Problems, CSP-graph based representation @conference{13-GonzalezCamacho-CEC, title = {A new CSP graph-based representation for Ant Colony Optimization}, author = {Antonio Gonzalez-Pardo and David Camacho}, url = {http://aida.ii.uam.es/wp-content/uploads/2013/07/CEC-13-GonzalezCamacho.pdf}, year = {2013}, date = {2013-05-13}, booktitle = {2013 IEEE Conference on Evolutionary Computation (CEC 2013)}, volume = {1}, pages = {689--696}, keywords = {Ant Colony Optimization, Computational Intelligence, Constraint Satisfaction Problems, CSP-graph based representation}, pubstate = {published}, tppubtype = {conference} } |