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2017 | 24 | Special Issue S3 |
Tytuł artykułu

Multi-AUV distributed task allocation based on the differential evolution quantum bee colony optimization algorithm

Autorzy
Warianty tytułu
Języki publikacji
EN
Abstrakty
EN
The multi-autonomous underwater vehicle (AUV) distributed task allocation model of a contract net, which introduces an equilibrium coefficient, has been established to solve the multi-AUV distributed task allocation problem. A differential evolution quantum artificial bee colony (DEQABC) optimization algorithm is proposed to solve the multi-AUV optimal task allocation scheme. The algorithm is based on the quantum artificial bee colony algorithm, and it takes advantage of the characteristics of the differential evolution algorithm. This algorithm can remember the individual optimal solution in the population evolution and internal information sharing in groups and obtain the optimal solution through competition and cooperation among individuals in a population. Finally, a simulation experiment was performed to evaluate the distributed task allocation performance of the differential evolution quantum bee colony optimization algorithm. The simulation results demonstrate that the DEQABC algorithm converges faster than the QABC and ABC algorithms in terms of both iterations and running time. The DEQABC algorithm can effectively improve AUV distributed multi-tasking performance
Słowa kluczowe
EN
Wydawca
-
Rocznik
Tom
24
Opis fizyczny
p.65-71,fig.,ref.
Twórcy
autor
  • College of Computer Science and Technology, Harbin Engineering University, Harbin, China
  • School of Computer and Information Engineering, Harbin University of Commerce,Harbin, China
autor
  • College of Computer Science and Technology, Harbin Engineering University, Harbin, China
  • College of Electromechanical and Information Engineering, Dalian Nationalities University, Dalian, China
autor
  • School of Computer and Information Engineering, Harbin University of Commerce,Harbin, China
Bibliografia
  • 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.
  • 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.
  • 3. Daqi Zhu, Huan Huang, and Simon X. Yang, 2013. Dynamic Task Assignment and Path Planning of MultiAUV 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.
  • 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.
  • 5. Parag C. Pendharkar, 2015. An ant colony optimization heuristic for constrained task alloc ation problem. Journal of Computational Science, 7(1), 37-47.
  • 6. Celal Özkale, Alpaslan Fığlalı, 2013. Evaluation of the multiobjective ant colony algorithm performances on biobjective quadratic assignment problems. Applied Mathematical Modelling, 37(1), 7822-7838.
  • 7. Zahra Beheshti, Siti Mariyam Shamsuddin, 2015. Nonparametric particle swarm optimization for global optimization. Applied Soft Computing, 28(2), 345-359.
  • 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.
  • 9. Eliseo Ferrante, Ali Emre Turgut, Edgar DuéñezGuzmán, Marco Dorigo,Tom Wenseleers,2015. Evolution of Self-Organized Task Specialization in Robot Swarms. Computational Biology, 10(3), 1371-1392.
  • 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.
  • 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.
  • 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.
  • 13. Hsing-Chih Tsai, 2014. Integrating the artificial bee colony and bees algorithm to face constrained optimization problems. Information Sciences, 258(2), 80-93.
  • 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.
  • 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.
  • 16. Peio Loubièrea, 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.
  • 17. D Karaboga, B Akay, 2009. A survey: algorithms simulating bee swarm intelligence. Artificial Intelligence Review, 31(1), 61-85.
  • 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.
  • 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.
  • 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.
  • 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.
  • 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.
  • 23. Bahriye Akay, Dervis Karaboga, 2012. Artificial bee colony has a differential evolution algorithm search strategy. Journal of Intelligent Manufacturing, 23(4), 1001-1014.
  • 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.
  • 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.
  • 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),21-57
Typ dokumentu
Bibliografia
Identyfikatory
Identyfikator YADDA
bwmeta1.element.agro-476acda8-d24f-4792-8da9-b7d0b3288516
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