554
Views
17
CrossRef citations to date
0
Altmetric
Original Articles

A strategic conflict avoidance approach based on cooperative coevolutionary with the dynamic grouping strategy

, , , , &
Pages 1995-2008 | Received 12 Dec 2013, Accepted 27 Jun 2014, Published online: 08 Dec 2014

References

  • Alam, S., Lokan, C., & Abbass, H.-A. (2012). What can make an airspace unsafe? Characterizing collision risk using multi-objective optimization. In Proceedings of the IEEE Congress on Evolutionary Computation. Brisbane.
  • Alam, S., Shafi, K., Abbass, H.-A, & Barlow, M. (2009). An ensemble approach for conflict detection in free flight by data mining. Transportation Research Part C, 17(3), 298–317.
  • Archibald, J.-K., Hill, J.-C., Jepsen, N.-A., Stirling, W.-C., & Frost, R.-L. (2008). A satisficing approach to aircraft conflict resolution. IEEE Transactions on Systems, Man and Cybernetics. Part C, Applications and Reviews, 38(4), 510–521.
  • Cai, K.-Q., Zhang, J., Zhou, C., Cao X.-B., & Tang, K. (2012). Using computational intelligence for large scale air route networks design. Applied Soft Computing, 12, 2790–2800.
  • Chen, X., Feng, L., & Soon Ong, Y. (2012). A self-adaptive memeplexes robust search scheme for solving stochastic demands vehicle routing problem. International Journal of Systems Science, 43(7), 1347–1366.
  • Durand, N., & Allignol, C. (2009). 4D-trajectory deconfliction through departure time adjustment. In Proceedings of the 8th USA/Europe Air Traffic Management R&D Seminar (ATM 2009). Napoli.
  • Durand, N., Allignol, C., & Barnier, N. (2010). A ground holding model for aircraft deconfliction. In Proceedings of the 29th IEEE/AIAA Digital Avionics Systems Conference (DASC 2010). Salt Lake City, UT.
  • Durand, N., Alliot, J.-M., & Noailles, J. (1996). Automatic aircraft conflict resolution using genetic algorithms. In Proceedings of the Symposium on Applied Computing. Philadelphia, PA.
  • Guan, X.-M., Zhang, X.-J., Han, D., Zhu, Y.-B, Lv J., & Su, J. (2013). A strategic flight conflict avoidance approach based on memetic algorithm. Chinese Journal of Aeronautics, 27(1), 93–101.
  • Hill, J.-C., Johnson F.-R., Archibald, J.-K., Frost, R.-L., & Stirling, W.-C. (2005). A cooperative multi-agent approach to free flight. In Proceedings of the Fourth International Joint Conference on Autonomous Agents and Multiagent System (AAMS 2005) (pp. 1083–1090). Utrecht.
  • Hwang, I., & Tomlin, C. (2002). Protocol-based conflict resolution for finite information horizon. In Proceedings of the American Control Conference (pp. 748–753). Alaska.
  • Kosecka, J., Tomlin, C., Pappas, G., & Sastry, S. (1997). Generation of conflict resolution maneuvers for air traffic management. In Proceedings of the IEEE/RSJ International Conference on Intelligent Robot and Systems (pp. 1598–1603). Grenoble.
  • Krozel, J., Peters, M., Bilimoria, K.-D., Lee, C., & Mitchell, J.-S.-B. (2001). System performance characteristics of centralized and decentralized air traffic separation strategies. Presented at the 4th USA/Europe Air Traffic Management R&D Seminar. Santa Fe, NM.
  • Kuchar, J., & Yang, L. (2000). A review of conflict detection and resolution modeling methods. IEEE Transactions on Intelligent Transportation Systems, 1(4), 179–189.
  • Liou, C.-D., Hsieh, Y.-C., & Chen, Y.-Y. (2013). A new encoding scheme-based hybrid algorithm for minimising two-machine flow-shop group scheduling problem. International Journal of Systems Science, 44(1), 77–93.
  • Liu, W., & Hwang, I. (2011). Probabilistic trajectory prediction and conflict detection for air traffic control. AIAA Journal of Guidance, Control and Dynamics, 34(6), 1779–1789.
  • Liu, Y., Yao, X., Zhao, Q., & Higuchi, T. (2001). Scaling up fast evolutionary programming with cooperative co-evolution. In Proceedings of the 2001 Congress on Evolutionary Computation (pp. 1101–1108). IEEE.
  • Mao, Z.-H., Dugail, D., & Feron, E. (2007). Space partition for conflict resolution of intersecting flows of mobile aircrafts. IEEE Transactions on Intelligent Transportation Systems, 8(3), 512–527.
  • Omidvar, M.-N., Li, X.-D., & Yao, X. (2010). Cooperative co-evolution with delta grouping for large scale non-separable function optimization. In Proceedings of the IEEE Congress on Evolutionary Computation (pp. 1762–1769). Barcelona: IEEE.
  • Pallottino, L., Feron, E.-M., & Bicchi, A. (2002). Conflict resolution problems for air traffic management systems solved with mixed integer programming. IEEE Transactions on Intelligent Transportation Systems, 3(1), 3–11.
  • Pěchouček, M., & Šišlák, D. (2009). Agent-based approach to free-flight planning, control, and simulation. IEEE Intelligent Systems, 24(1), 14–17.
  • Pham, V., Bui, L., Alam, S., Lokan, C., & Abbass, H.-A. (2010). A Pittsburgh multi-objective classifier for user preferred trajectories and flight navigation. In Proceedings of the IEEE Congress on Evolutionary Computation (pp. 608–615). Barcelona: IEEE.
  • Potter, M., & Jong, K.-D. (1994). A cooperative coevolutionary approach to function optimization. In Proceedings of the Third Conference on Parallel Problem Solving from Nature (pp. 249–257). Jerusalem: Springer.
  • Ray, T., & Yao, X. (2009). A cooperative coevolutionary algorithm with correlation based adaptive variable partitioning. In Proceedings of the Eleventh Congress on Evolutionary Computation (pp. 983–989). Trondheim: IEEE.
  • Rong, J., Geng, S., Valasek, J., & Ioerger, T.-R. (2002). Air traffic control negotiation and resolution using an on board multi-aircraft system. In Proceedings of the 21st Digital Avionics System Conference (pp. 7B2/1–12). Irvine, CA: IEEE.
  • Sayed, E., Essam, D., and Sarker, R.-A. (2012a). Dependency identification technique for large scale optimization problems. In Proceedings of the IEEE Congress on Evolutionary Computation (pp. 1–8). Irvine, CA: IEEE.
  • Sayed, E., Essam, D., & Sarker, R.-A. (2012b). Using hybrid dependency identification with a memetic algorithm for large scale optimization problems. Lecture Notes in Computer Science, 7673, 168–177.
  • Shi, Y., Teng, H., & Li, Z. (2005). Cooperative co-evolutionary differential evolution for function optimization’. In Proceedings of the First International Conference on Natural Computation (pp. 1080–1088). Changsha.
  • Sofge, D., Jong, K.-D., & Schultz, A. (2002). A blended population approach to cooperative coevolution for decomposition of complex problems. In Proceedings of the 2002 Congress on Evolutionary Computation (pp. 413–418). Honolulu, HI: IEEE.
  • Su, J., Zhang, X.-J., & Guan, X.-M. (2013). 4D-trajectory conflict resolution using cooperative coevolution. In Proceedings of the 2012 International Conference on Information Technology and Software Engineering (ITSE 2012) (pp. 387–395). Beijing: Springer-Verlag.
  • Tomlin, C., Mitchell, I., & Ghosh, R. (2001). Safety verification of conflict resolution manoeuvres. IEEE Transactions on Intelligent Transportation Systems, 2(2), 110–120.
  • Wang, Y., Li, B., & Lai, X.-X. (2009). Variance priority based cooperative co-evolution differential evolution for large scale global optimization. In Proceedings of the 2009 IEEE Congress On Evolutionary Computation (CEC 2009) (pp. 1232–1239). Trondheim: IEEE.
  • Wollkind, S., Valasek, J., & Ioerger, T. (2004). Automated conflict resolution for air traffic management using cooperative multi-aircraft negotiation. Paper presented at the AIAA Guidance, Navigation, Control Conference and Exhibition. Providence, RI.
  • Yang, Z., Tang, K., & Yao, X. (2008). Large scale evolutionary optimization using cooperative co-evolution. Information Sciences, 178(15), 2985–299.
  • Zheng, C., Ding, M., Zhou, C., & Li, L. (2004). Coevolving and cooperating path planner for multiple unmanned air vehicles. Engineering Applications of Artificial Intelligence, 44(8), 887–896.
  • Zhu, F.-M., & Guan, S.-U. (2008). Cooperative co-evolution of GA-based classifiers based on input decomposition. Engineering Applications of Artificial Intelligence, 21(8), 887–896.
  • Zhu, Z.-X, Wang, F.-X., He, S., & Sun, Y.-W (2013). Global path planning of mobile robots using a memetic algorithm. International Journal of Systems Science. doi:10.1080/00207721.2013.843735

Reprints and Corporate Permissions

Please note: Selecting permissions does not provide access to the full text of the article, please see our help page How do I view content?

To request a reprint or corporate permissions for this article, please click on the relevant link below:

Academic Permissions

Please note: Selecting permissions does not provide access to the full text of the article, please see our help page How do I view content?

Obtain permissions instantly via Rightslink by clicking on the button below:

If you are unable to obtain permissions via Rightslink, please complete and submit this Permissions form. For more information, please visit our Permissions help page.