Hostname: page-component-cb9f654ff-qc88w Total loading time: 0 Render date: 2025-09-01T12:46:35.337Z Has data issue: false hasContentIssue false
  • English
  • Français

A multi-objective evolutionary hyper-heuristic algorithm for team-orienteering problem with time windows regarding rescue applications

Part of: Robot Athletes and Entertainers

Published online by Cambridge University Press:  02 December 2019

Hadi S. Aghdasi Open the ORCID record for Hadi S. Aghdasi [Opens in a new window] ,
Saeed Saeedvand  and
Jacky Baltes
Hadi S. Aghdasi
Affiliation:
Faculty of Electrical and Computer Engineering, University of Tabriz, Tabriz, Iran e-mails: aghdasi@tabrizu.ac.ir, saeedvand@tabrizu.ac.ir
Saeed Saeedvand
Affiliation:
Faculty of Electrical and Computer Engineering, University of Tabriz, Tabriz, Iran e-mails: aghdasi@tabrizu.ac.ir, saeedvand@tabrizu.ac.ir
Jacky Baltes
Affiliation:
Department of Electrical Engineering, National Taiwan Normal University, Taipei, Taiwan e-mail: jacky.baltes@ntnu.edu.tw
Get access Rights & Permissions [Opens in a new window]

Abstract

The team-orienteering problem (TOP) has broad applicability. Examples of possible uses are in factory and automation settings, robot sports teams, and urban search and rescue applications. We chose the rescue domain as a guiding example throughout this paper. Hence, this paper explores a practical variant of TOP with time window (TOPTW) for rescue applications by humanoid robots called TOPTWR. Due to the significant range of algorithm choices and their parameters tuning challenges, the use of hyper-heuristics is recommended. Hyper-heuristics can select, order, or generate different low-level heuristics with different optimization algorithms. In this paper, first, a general multi-objective (MO) solution is defined, with five objectives for TOPTWR. Then a robust and efficient MO and evolutionary hyper-heuristic algorithm for TOPTW based on the humanoid robot’s characteristics in the rescue applications (MOHH-TOPTWR) is proposed. MOHH-TOPTWR includes two MO evolutionary metaheuristics algorithms (MOEAs) known as non-dominated sorting genetic algorithm (NSGA-III) and MOEA based on decomposition (MOEA/D). In this paper, new benchmark instances are proposed for rescue applications using the existing ones for TOPTW. The experimental results show that MOHH-TOPTWR in both MOEAs can outperform all the state-of-the-art algorithms as well as NSGA-III and MOEA/D MOEAs.

Information

Type
Research Article
Information
The Knowledge Engineering Review , Volume 34 , 2019 , e19
Copyright
© Cambridge University Press, 2019 

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

Article purchase

Temporarily unavailable

References

Abbaszadeh, M. & Saeedvand, S. 2014. A fast genetic algorithm for solving university scheduling problem. IAES International Journal of Artificial Intelligence 3, 7.CrossRefGoogle Scholar
Abbaszadeh, M., Saeedvand, S. & Mayani, H. A. 2012. Solving university scheduling problem with a memetic algorithm. IAES International Journal of Artificial Intelligence 1, 79.Google Scholar
Alkhanak, E. N. & Lee, S. P. 2018. A hyper-heuristic cost optimisation approach for scientific workflow scheduling in cloud computing. Future Generation Computer Systems 86, 480–506.CrossRefGoogle Scholar
Auer, P., Cesa-Bianchi, N. & Fischer, P. 2002. Finite-time analysis of the multiarmed bandit problem. Machine Learning 47, 235–256.CrossRefGoogle Scholar
Baltes, J., Tu, K.-Y., Sadeghnejad, S. & Anderson, J. 2017. HuroCup: Competition for multi-event humanoid robot athletes. The Knowledge Engineering Review 32, 1–14.CrossRefGoogle Scholar
Bederina, H. & Hifi, M. 2017. A hybrid multi-objective evolutionary algorithm for the team orienteering problem. In 2017 4th International Conference on Control, Decision and Information Technologies (CoDIT), 0898–0903. IEEE.CrossRefGoogle Scholar
Bottarelli, L., Bicego, M., Blum, J. & Farinelli, A. 2019. Orienteering-based informative path planning for environmental monitoring. Engineering Applications of Artificial Intelligence 77, 46–58.CrossRefGoogle Scholar
Burke, E. K., Gendreau, M., Hyde, M., Kendall, G., Ochoa, G., Özcan, E. & Qu, R. 2013. Hyper-heuristics: a survey of the state of the art. Journal of the Operational Research Society 64, 1695–1724.CrossRefGoogle Scholar
Burke, E. K., Hyde, M., Kendall, G., Ochoa, G., Özcan, E. & Woodward, J. R. 2010. A classification of hyper-heuristic approaches. In Handbook of Metaheuristics, Gendreau, M. & Potvin, JY. (eds). Springer.Google Scholar
Campbell, A. M., Gendreau, M. & Thomas, B. W. 2011. The orienteering problem with stochastic travel and service times. Annals of Operations research 186, 61–81.CrossRefGoogle Scholar
Chakhlevitch, K. & Cowling, P. 2008. Hyperheuristics: Recent developments. In Adaptive and Multilevel Metaheuristics, Cotta, C., Sevaux, M. & Sörensen, K. (eds). Springer.Google Scholar
Chang, C.-H., Wang, S.-C. & Wang, C.-C. 2016. Exploiting moving objects: multi-robot simultaneous localization and tracking. IEEE Transactions on Automation Science and Engineering 13, 810–827.CrossRefGoogle Scholar
Coello, C. A. C., Lamont, G. B. & Van Veldhuizen, D. A. 2007. Evolutionary Algorithms for Solving Multi-Objective Problems. Springer.Google Scholar
Cordeau, J.‐F., Gendreau, M. & Laporte, G. 1997. A tabu search heuristic for periodic and multi‐depot vehicle routing problems. Networks: An International Journal 30, 105–119.3.0.CO;2-G>CrossRefGoogle Scholar
Cura, T. 2014. An artificial bee colony algorithm approach for the team orienteering problem with time windows. Computers & Industrial Engineering 74, 270–290.CrossRefGoogle Scholar
Deb, K. & Jain, H. 2014. An evolutionary many-objective optimization algorithm using reference-point-based nondominated sorting approach, part I: Solving problems with box constraints. IEEE Transactions on Evolutionary Computation 18, 577–601.CrossRefGoogle Scholar
Deb, K., Pratap, A., Agarwal, S. & Meyarivan, T. A. M. T. 2002. A fast and elitist multiobjective genetic algorithm: NSGA-II. IEEE Transactions on Evolutionary Computation 6, 182–197.CrossRefGoogle Scholar
DeDonato, M., Dimitrov, V., Du, R., Giovacchini, R., Knoedler, K., Long, X., Polido, F., Gennert, M. A., Padır, T. & Feng, S. 2015. Human‐in‐the‐loop control of a humanoid robot for disaster response: a report from the DARPA robotics challenge trials. Journal of Field Robotics 32, 275–292.CrossRefGoogle Scholar
Diftler, M. A., Culbert, C. J., Ambrose, R. O., Platt, R. & Bluethmann, W. J. 2003. Evolution of the NASA/DARPA robonaut control system. In ‘2003 Proceedings of IEEE International Conference on Robotics and Automation ICRA’03, 2543–2548. IEEE.Google Scholar
Dong, N. & Dai, C. 2018. An improvement decomposition-based multi-objective evolutionary algorithm using multi-search strategy. Knowledge-Based Systems 163, 572–580.CrossRefGoogle Scholar
Duchoň, F., Babinec, A., Kajan, M., Beňo, P., Florek, M., Fico, T. & Jurišica, L. 2014. Path planning with modified a star algorithm for a mobile robot. Procedia Engineering 96, 59–69.CrossRefGoogle Scholar
Farinelli, A., Zanotto, E. & Pagello, E. 2017. Advanced approaches for multi-robot coordination in logistic scenarios. Robotics and Autonomous Systems 90, 34–44.CrossRefGoogle Scholar
Feng, S., Whitman, E., Xinjilefu, X. & Atkeson, C. G. 2015. Optimization‐based full body control for the DARPA robotics challenge. Journal of Field Robotics 32, 293–312.CrossRefGoogle Scholar
Fialho, Á., Da Costa, L., Schoenauer, M. & Sebag, M. 2010. Analyzing bandit-based adaptive operator selection mechanisms. Annals of Mathematics and Artificial Intelligence 60, 25–64.CrossRefGoogle Scholar
Goldberg, D. E. 1989 Genetic Algorithms in Search, Optimization, and Machine Learning, Addison-Wesley, Reading, Ma, 1989. Addison-Wesley Longman Publishing.Google Scholar
Golden, B. L., Levy, L. & Vohra, R. 1987. The orienteering problem. Naval Research Logistics (NRL) 34, 307–318.3.0.CO;2-D>CrossRefGoogle Scholar
Guizzo, G., Vergilio, S. R., Pozo, A. T. & Fritsche, G. M. 2017. A multi-objective and evolutionary hyper-heuristic applied to the integration and test order problem. Applied Soft Computing 56, 331–344.CrossRefGoogle Scholar
Gunawan, A., Lau, H. C. & Lu, K. 2018. ADOPT: combining parameter tuning and adaptive operator ordering for solving a class of orienteering problems. Computers & Industrial Engineering 121, 82–96.CrossRefGoogle Scholar
Gunawan, A., Lau, H. C. & Vansteenwegen, P. 2016. Orienteering problem: a survey of recent variants, solution approaches and applications. European Journal of Operational Research 255, 315–332.CrossRefGoogle Scholar
Gunawan, A., Lau, H. C., Vansteenwegen, P. & Lu, K. 2017. Well-tuned algorithms for the team orienteering problem with time windows. Journal of the Operational Research Society 68, 861–876.CrossRefGoogle Scholar
Gunn, T. & Anderson, J. 2015. Dynamic heterogeneous team formation for robotic urban search and rescue. Journal of Computer and System Sciences 81, 553–567.CrossRefGoogle Scholar
Hu, Q. & Lim, A. 2014. An iterative three-component heuristic for the team orienteering problem with time windows. European Journal of Operational Research 232, 276–286.CrossRefGoogle Scholar
Huang, L., Ding, Y. & Jin, Y. 2018. Multiple-solution optimization strategy for multi-robot task allocation. IEEE Transactions on Systems, Man and Cybernetics: Systems.CrossRefGoogle Scholar
Jiang, Y. 2016. A survey of task allocation and load balancing in distributed systems. IEEE Transactions on Parallel and Distributed Systems 27, 585–599.CrossRefGoogle Scholar
Jin, M., Lee, J. & Tsagarakis, N. G. 2017. Model-free robust adaptive control of humanoid robots with flexible joints. IEEE Transactions on Industrial Electronics 64, 1706–1715.CrossRefGoogle Scholar
Jose, K. & Pratihar, D. K. 2016. Task allocation and collision-free path planning of centralized multi-robots system for industrial plant inspection using heuristic methods. Robotics and Autonomous Systems 80, 34–42.CrossRefGoogle Scholar
Kaneko, K., Morisawa, M., Kajita, S., Nakaoka, S. I., Sakaguchi, T., Cisneros, R. & Kanehiro, F. 2015. Humanoid robot HRP-2Kai—Improvement of HRP-2 towards disaster response tasks. In ‘2015 IEEE-RAS 15th International Conference on Humanoid Robots (Humanoids), 132–139. IEEE.CrossRefGoogle Scholar
Karakatič, S. & Podgorelec, V. 2015. A survey of genetic algorithms for solving multi depot vehicle routing problem. Applied Soft Computing 27, 519–532.CrossRefGoogle Scholar
Khamis, A., Hussein, A. & Elmogy, A. 2015. Multi-robot task allocation: a review of the state-of-the-art. In Cooperative Robots and Sensor Networks. Springer.Google Scholar
Kohlbrecher, S., Romay, A., Stumpf, A., Gupta, A., Von Stryk, O., Bacim, F., Bowman, D. A., Goins, A., Balasubramanian, R. & Conner, D. C. 2015. Human‐robot teaming for rescue missions: team ViGIR’s approach to the 2013 DARPA robotics challenge trials. Journal of Field Robotics 32, 352–377.CrossRefGoogle Scholar
Koubaa, A., Bennaceur, H., Chaari, I., Trigui, S., Ammar, A., Sriti, M. F., Alajlan, M., Cheikhrouhou, O. & Javed, Y. 2018. Different approaches to solve the MRTA problem. In Robot Path Planning and Cooperation, Kacprzyk, J. (ed.). Springer.CrossRefGoogle Scholar
Kube, C. R. & Bonabeau, E. 2000. Cooperative transport by ants and robots. Robotics and Autonomous Systems 30, 85–101.CrossRefGoogle Scholar
Labadie, N., Mansini, R., Melechovský, J. & Calvo, R. W. 2012. The team orienteering problem with time windows: an lp-based granular variable neighborhood search. European Journal of Operational Research 220, 15–27.CrossRefGoogle Scholar
Lin, S.-W. & Vincent, F. Y. 2012. A simulated annealing heuristic for the team orienteering problem with time windows. European Journal of Operational Research 217, 94–107.CrossRefGoogle Scholar
Lin, S.-W. & Vincent, F. Y. 2017. Solving the team orienteering problem with time windows and mandatory visits by multi-start simulated annealing. Computers & Industrial Engineering 114, 195–205.CrossRefGoogle Scholar
Mahajan, A. & Teneketzis, D. 2008. Multi-armed bandit problems. In Foundations and Applications of Sensor Management, Hero, AO., Castañón, D., Cochran, D. & Kastella, K. (eds). Springer.Google Scholar
Martín-Moreno, R. & Vega-Rodríguez, M. A. 2018. Multi-objective artificial bee colony algorithm applied to the bi-objective orienteering problem. Knowledge-Based Systems 154, 93–101.CrossRefGoogle Scholar
Michalewicz, Z. 2013. Genetic Algorithms+ Data Structures= Evolution Programs. Springer Science & Business Media.Google Scholar
Montemanni, R. & Gambardella, L. M. 2009. An ant colony system for team orienteering problems with time windows. Foundation Of Computing And Decision Sciences 34, 287.Google Scholar
Nunes, E., Manner, M., Mitiche, H. & Gini, M. 2017. A taxonomy for task allocation problems with temporal and ordering constraints. Robotics and Autonomous Systems 90, 55–70.CrossRefGoogle Scholar
Park, J., Lee, J., Ahn, S., Bae, J. & Tae, H. 2017. Exact algorithm for the capacitated team orienteering problem with time windows. Mathematical Problems in Engineering 2017, 1191–1203.CrossRefGoogle Scholar
Righini, G. & Salani, M. 2009. Decremental state space relaxation strategies and initialization heuristics for solving the orienteering problem with time windows with dynamic programming. Computers & Operations Research 36, 1191–1203.CrossRefGoogle Scholar
Saeedvand, S. & Aghdasi, H. S. 2016. An energy efficient metaheuristic method for micro robots indoor area coverage problem. In ‘2016 6th International Conference on Computer and Knowledge Engineering (ICCKE), 88–93. IEEE.CrossRefGoogle Scholar
Saeedvand, S., Aghdasi, H. S. & Baltes, J. 2018. Novel lightweight odometric learning method for humanoid robot localization. Mechatronics 55, 38–53.CrossRefGoogle Scholar
Saeedvand, S., Aghdasi, H. S. & Baltes, J. 2019. Robust multi-objective multi-humanoid robots task allocation based on novel hybrid metaheuristic algorithm. Applied Intelligence 49, 4097–4127.CrossRefGoogle Scholar
Savelsbergh, M. W. P. 1985. Local search in routing problems with time windows. Annals of Operations Research 4, 285–305.CrossRefGoogle Scholar
Schilde, M., Doerner, K. F., Hartl, R. F. & Kiechle, G. 2009. Metaheuristics for the bi-objective orienteering problem. Swarm Intelligence 3, 179–201.CrossRefGoogle Scholar
Schwarzrock, J., Zacarias, I., Bazzan, A. L., de Araujo Fernandes, R. Q., Moreira, L. H. & de Freitas, E. P. 2018. Solving task allocation problem in multi unmanned aerial vehicles systems using swarm intelligence. Engineering Applications of Artificial Intelligence 72, 10–20.CrossRefGoogle Scholar
Solomon, M. M. 1986. On the worst‐case performance of some heuristics for the vehicle routing and scheduling problem with time window constraints. Networks 16, 161–174.CrossRefGoogle Scholar
Solomon, M. M. 1987. Algorithms for the vehicle routing and scheduling problems with time window constraints. Operations Research 35, 254–265.CrossRefGoogle Scholar
Souffriau, W., Vansteenwegen, P., Vanden Berghe, G. & Van Oudheusden, D. 2013. The multiconstraint team orienteering problem with multiple time windows. Transportation Science 47, 53–63.CrossRefGoogle Scholar
Spenko, M., Buerger, S. & Iagnemma, K. 2018. The DARPA Robotics Challenge Finals: Humanoid Robots To The Rescue. Springer.CrossRefGoogle Scholar
Su, X., Wang, Y., Jia, X., Guo, L. & Ding, Z. 2018. Two innovative coalition formation models for dynamic task allocation in disaster rescues. Journal of Systems Science and Systems Engineering 27, 215–230.CrossRefGoogle Scholar
Tang, H. & Miller-Hooks, E. 2005. A tabu search heuristic for the team orienteering problem. Computers & Operations Research 32, 1379–1407.CrossRefGoogle Scholar
Toledo, A. & Riff, M. C. 2015. HOPHS: a hyperheuristic that solves orienteering problem with hotel selection. In ‘2015 Fifth International Conference on Digital Information Processing and Communications (ICDIPC), 148–152. IEEE.CrossRefGoogle Scholar
Tricoire, F., Romauch, M., Doerner, K. F. & Hartl, R. F. 2010. Heuristics for the multi-period orienteering problem with multiple time windows. Computers & Operations Research 37, 351–367.CrossRefGoogle Scholar
Tsiligirides, T. 1984. Heuristic methods applied to orienteering. Journal of the Operational Research Society 35, 797–809.CrossRefGoogle Scholar
Vansteenwegen, P., Souffriau, W., Berghe, G. V. & Van Oudheusden, D. 2009. Iterated local search for the team orienteering problem with time windows. Computers & Operations Research 36, 3281–3290.CrossRefGoogle Scholar
Vansteenwegen, P., Souffriau, W. & Van Oudheusden, D. 2011. The orienteering problem: a survey. European Journal of Operational Research 209, 1–10.CrossRefGoogle Scholar
Vincent, F. Y., Jewpanya, P., Ting, C. J. & Redi, A. P. 2017. Two-level particle swarm optimization for the multi-modal team orienteering problem with time windows. Applied Soft Computing 61, 1022–1040.Google Scholar
Wang, J., Zhou, Y., Wang, Y., Zhang, J., Chen, C. P. & Zheng, Z. 2016. Multiobjective vehicle routing problems with simultaneous delivery and pickup and time windows: formulation, instances, and algorithms. IEEE Transactions on Cybernetics 46, 582–594.CrossRefGoogle ScholarPubMed
Yang, X.-S. 2010. Nature-Inspired Metaheuristic Algorithms. Luniver Press.Google Scholar
Yin, P.-Y., Yu, S. S., Wang, P. P. & Wang, Y. T. 2007. Multi-objective task allocation in distributed computing systems by hybrid particle swarm optimization. Applied Mathematics and Computation 184, 407–420.CrossRefGoogle Scholar
Zhang, Q. & Li, H. 2007. MOEA/D: a multiobjective evolutionary algorithm based on decomposition. IEEE Transactions on Evolutionary Computation 11, 712–731.CrossRefGoogle Scholar
Zhang, Q., Zhou, A. & Jin, Y. 2008. RM-MEDA: a regularity model-based multiobjective estimation of distribution algorithm. IEEE Transactions on Evolutionary Computation 12, 41–63.CrossRefGoogle Scholar
Zhang, T. & Ueno, H. 2007. Knowledge model-based heterogeneous multi-robot system implemented by a software platform. Knowledge-Based Systems 20, 310–319.CrossRefGoogle Scholar
Zheng, W., Liao, Z. & Qin, J. 2017. Using a four-step heuristic algorithm to design personalized day tour route within a tourist attraction. Tourism Management 62, 335–349.CrossRefGoogle Scholar
Zhu, W., Li, L., Teng, L. & Yonglu, W. 2018. Multi-UAV reconnaissance task allocation for heterogeneous targets using an opposition-based genetic algorithm with double-chromosome encoding. Chinese Journal of Aeronautics 31, 339–350.Google Scholar
Zitzler, E. 1999. Evolutionary Algorithms for Multiobjective Optimization: Methods and Applications. Citeseer.Google Scholar
Zitzler, E. & Thiele, L. 1999. Multiobjective evolutionary algorithms: a comparative case study and the strength Pareto approach. IEEE Transactions on Evolutionary Computation 3, 257–271.CrossRefGoogle Scholar
Zitzler, E., Thiele, L., Laumanns, M., Fonseca, C. M. & Da Fonseca, V. G. 2003. Performance assessment of multiobjective optimizers: an analysis and review. IEEE Transactions on Evolutionary Computation 7, 117–132.CrossRefGoogle Scholar