Multi-Auv Distributed Task Allocation Based on the Differential Evolution Quantum Bee Colony Optimization Algorithm

1. B He, L Ying, S Zhang, X Feng, R Nian, 2015. Autonomous navigation based on unscent ed-FastSL AM using particle swarm optimization for autonomous underwater vehicles. Meas rement, 71(1), 89-101.10.1016/j.measurement.2015.02.026Search in Google Scholar

2. Y Shen, H Zhang, B He, T Yan, 2015. Autonomous Navigation Based on SEIF with Consistency Constraint for C-Ranger AUV. Mathematical Problems in Engineering, 3(1), 231-243.10.1155/2015/752360Search in Google Scholar

3. Daqi Zhu, Huan Huang, and Simon X. Yang, 2013. Dynamic Task Assignment and Path Planning of Multi- AUV System Based on an Improved Self-Organizing Map and Velo city Synthesis Method in Three-Dimensional Underwater Workspace. IEEE Transactions on Cybernetics, 43(2), 504-514.10.1109/TSMCB.2012.221021222949070Search in Google Scholar

4. DF Yuan, L Cong-Ying, 2013.Application of Improved Ant Colony Algorithm for Quadrat ic Assignment Problems. Computer and Modernization, 3(1), 9-11.Search in Google Scholar

5. Parag C. Pendharkar, 2015. An ant colony optimization heuristic for constrained task alloc ation problem. Journal of Computational Science, 7(1), 37-47.10.1016/j.jocs.2015.01.001Search in Google Scholar

6. Celal Ozkale, Alpaslan Fığlalı, 2013. Evaluation of the multiobjective ant colony algorithm performances on biobjective quadratic assignment problems. Applied Mathematical Modelling, 37(1), 7822-7838.10.1016/j.apm.2013.01.045Search in Google Scholar

7. Zahra Beheshti, Siti Mariyam Shamsuddin, 2015. Nonparametric particle swarm optimization for global optimization. Applied Soft Computing, 28(2), 345-359.10.1016/j.asoc.2014.12.015Search in Google Scholar

8. AI Awad, NA El-Hefnawy, HM Abdel_Kader, 2015. Enhanced Particle Swarm Optimization for Task Scheduling in Cloud Computing Environments. Procedia Computer Science, 35(1), 920-929.10.1016/j.procs.2015.09.064Search in Google Scholar

9. Eliseo Ferrante, Ali Emre Turgut, Edgar Duenez- Guzman, Marco Dorigo,Tom Wenseleers,2015. Evolution of Self-Organized Task Specialization in Robot Swarms. Computational Biology, 10(3), 1371-1392.Search in Google Scholar

10. Christina M. Grozinger, Jessica Richards, Heather R. Mattila, 2014. From molecules to societies: mechanisms regulating swarming behavior in honey bees. Apidologie, 45(3), 327-346.10.1007/s13592-013-0253-2Search in Google Scholar

11. D Karaboga, Basturk, 2007.A powerful and efficient algorithm for numerical function optimization: artificial bee colony (ABC) algorithm. Journal of Global Optimization, 39(3), 459-471.10.1007/s10898-007-9149-xOpen DOISearch in Google Scholar

12. R Akbari, A Mohammadi, K Ziarati, 2010. A novel bee swarm optimization algorithm for numerical function optimization. Communications in Nonlinear Science and Numerica Simulat, 15(5), 3142-3155.10.1016/j.cnsns.2009.11.003Search in Google Scholar

13. Hsing-Chih Tsai, 2014. Integrating the artificial bee colony and bees algorithm to face constrained optimization problems. Information Sciences, 258(2), 80-93.10.1016/j.ins.2013.09.015Search in Google Scholar

14. Dervis Karaboga, Beyza Gorkemli, Celal Ozturk,Nurhan Karaboga, 2014. A comprehensive survey: artificial bee colony (ABC) algorithm and applications. Artificial Intelligence Review, 42(1), 21-57.10.1007/s10462-012-9328-0Open DOISearch in Google Scholar

15. Pinar Civicioglu, Erkan Besdok, 2013. A conceptual comparison of the Cuckoo-search, particle swarm optimization, differential evolution and artificial bee colony algorithms. Artificial Intelligence Review, 39(2), 315-346.10.1007/s10462-011-9276-0Open DOISearch in Google Scholar

16. Peio Loubierea, Astrid Jourdana, Patrick Siarryb, achid Chelouaha, 2016. A sensitivity analysis method for driving the Artificial Bee Colony algorithm’s search process. Applied Soft Computing, 41(1), 515-531.10.1016/j.asoc.2015.12.044Open DOISearch in Google Scholar

17. D Karaboga, B Akay, 2009. A survey: algorithms simulating bee swarm intelligence. Artificial Intelligence Review, 31(1), 61-85. 10.1007/s10462-009-9127-4Open DOISearch in Google Scholar

18. Celal Ozturk, Emrah Hancer, Dervis Karaboga, 2015. Improved clustering criterion for image clustering with artificial bee colony algorithm. Pattern Analysis and Applications, 18(3), 587-599.10.1007/s10044-014-0365-ySearch in Google Scholar

19. J Sun, W Fang, X Wu,2014. Quantum-Behaved Particle Swarm Optimization: Analysis of Individual Particle Behavior and Parameter Selection. Evolutionary Computation, 20(3), 349-393.10.1162/EVCO_a_0004921905841Search in Google Scholar

20. Miha Mlakar, Dejan Petelin, Tea Tušar, Bogdan Filipič, 2015. GP-DEMO: Differential evolution for multiobjective optimization based on Gaussian process models. European Journal of Operational Research, 243(2), 347-361.10.1016/j.ejor.2014.04.011Search in Google Scholar

21. A. C. Biju, T. Aruldoss Albert Victoire, and Kumaresan Mohanasundaram, 2015. An Improved Differential Evolution Solution for Software Project Scheduling Problem. Scientific World Journal, 2(1), 1-9.10.1155/2015/232193460604326495419Search in Google Scholar

22. Sk. Minhazul Islam, Swagatam Das, 2012. An Adaptive Differential Evolution Algorithm With Novel Mutation and Crossover Strategies for Global Numerical Optimization. IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics), 42(2), 482-500.10.1109/TSMCB.2011.216796622010153Search in Google Scholar

23. Bahriye Akay, Dervis Karaboga, 2012. Artificial bee colony has a differential evolution algorithm search strategy. Journal of Intelligent Manufacturing, 23(4), 1001-1014.Search in Google Scholar

24. A Bouaziz, A Draa, S Chikhi, 2013. A Quantum-inspired Artificial Bee Colony algorithm for numerical optimization. In: International Symposium on Programming & Systems. Algiers Algeria. pp. 81-88.10.1109/ISPS.2013.6581498Search in Google Scholar

25. X li, M yin, 2014. Parameter estimation for chaotic systems by hybrid differential evolution algorithm and artificial bee colony algorithm. Nonlinear Dynamics, 77(1), 61-71.10.1007/s11071-014-1273-9Search in Google Scholar

26. D Karaboga, B Gorkemli, C Ozturk, N Karaboga, 2014. A comprehensive survey: artificial bee colony (ABC) algorithm and applications. Artificial Intelligence Review, 42(1),21-5710.1007/s10462-012-9328-0Open DOISearch in Google Scholar