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JACIII Vol.19 No.4 pp. 491-499
doi: 10.20965/jaciii.2015.p0491
(2015)

Paper:

A Heuristic Algorithm Based on Leadership Strategy: Leader of Dolphin Herd Algorithm (LDHA)

Jianqiang Zhao, Kao Ge, and Kangyao Xu

School of Mathematic and Physical Science, Xuzhou Institute of Technology
Xuzhou 221111, China

Received:
August 5, 2014
Accepted:
March 26, 2015
Published:
July 20, 2015
Keywords:
leader of dolphin herd algorithm, heuristic algorithm, trial function convergence
Abstract

A heuristic algorithm named the leader of dolphin herd algorithm (LDHA) is proposed in this paper to solve an optimization problem whose dimensionality is not high, with dolphins that imitate predatory behavior. LDHA is based on a leadership strategy. Using the leadership strategy as reference, we have designed the proposed algorithm by simulating the preying actions of dolphin herds. Several intelligent behaviors, such as “producing leaders,” “group gathering,” “information sharing,” and “rounding up prey,” are abstracted by LDHA. The proposed algorithm is tested on 15 typical complex function optimization problems. The testing results reveal that compared with the particle swarm optimization and the genetic algorithms, LDHA has relatively high optimization accuracy and capability for complex functions. Further, it is almost unaffected by the inimicality, multimodality, or dimensions of functions in the function optimization section, which implies better convergence. In addition, ultra-high-dimensional function optimization capabilities of this algorithm were tested using the IEEE CEC 2013 global optimization benchmark. Unfortunately, the proposed optimization algorithm has a limitation in that it is not suitable for ultra-high-dimensional functions.

References
  1. [1]  J. Kennedy and R. C. Eberhart, “Particle swarm optimization,” Proc. of IEEE Int. Conf. on Neural Networks, pp. 1942-1948, 1995.
  2. [2]  M. Dorigo, V. Maniezzo, and A. Colorni, “Ant system:optimization by a colony of cooperating agent,” IEEE Trans. on Systems, Man, and Cybernetics, Vol.26, No.1, pp. 29-41, 1996.
  3. [3]  X. L. Li, Z. J. Shao, and J. X. Qian, “An optimizing method based on autonomous animats:fishswarm algorithm,” Systems Engineering Theory and Practice, Vol.22, No.11, pp. 32-38, 2002.
  4. [4]  K. Passino, “Biomimicry of bacterial foraging for distributed optimization and control,” IEEE Control Systems Magazine, Vol.22, No.2, pp. 52-67, 2002.
  5. [5]  M. M. Eusuff and K. E. Lansey, “Optimization of water distribution network design using the shuffled frog leaping algorithm,” J. of Water Resources Planning and Management, Vol.129, No.2, pp. 210-225, 2003.
  6. [6]  D. Karaboga and B. Basturk, “A powerful andefficient algorithm for numerical function optimization: Artificial bee colony(abc)algorithm,” J. of Golbal Optimization, Vol.39, No.2, pp. 459-471, 2007.
  7. [7]  A. Jorge, D. P. Ocotláan, C. Felipe, et al., “Meta-Heuristics Algorithms based on the Grouping of Animals by Social Behavior for the Traveling Salesman Problem,” Int. J. of Combinatorial Optimization Problems and Informatics, Vol.3, No.2, pp. 104-123, 2012.
  8. [8]  Z.-Y. Li, L. Ma, and H.-Z. Zhang, “Cellular bat algorithm for 0-1 programming problem,” Application Research of Computers, Vol.30, No.10, pp. 2093-2035, 2013.
  9. [9]  H. S. Wu, F. M. Zhang, and L. S. Wu, “New swarmintelligence algorithm-wolf pack algorithm,” Systems Engineering and electronics, Vol.35, No.11, pp. 3430-3438, 2013.
  10. [10]  T. Back, “Evolutionary algorithms in theory and practice,” Oxford University Press, pp. 21-28, 1996.
  11. [11]  K. Dervis and A. Bahriye, “A comparative study of Artificial Bee Colony algorithm,” Applied Mathematics and Computation, Vol.90, No.2, pp. 1-25, 2009.
  12. [12]  M. Q. Hu, T. Wu, and J. D. Weir, “An intelligentaugmentation of particle swarm optimization with multiple adaptive methods,” Information Sciences, Vol.20, No.4, pp. 68-83, 2012.
  13. [13]  M. Saeed and S. Z. Hamid, “Improved particle swarmoptimization and applications toHiddenmarkov model and ackley function,” Proc. of IEEE Int. Conf. on Computational Intelligence for Measurement Systems and Applications, pp. 146-169, 2011.
  14. [14]  Q. Tang, Y. Shen, C. Y. Hu, et al., “Swarm Intelligence:Based Cooperation Optimization of Multi-Modal Functions,” Cognitive Computation, Vol.5, No.1, pp. 48-55, 2013.
  15. [15]  R. S. Parpinelli, F. R. Teodoro, and H. S. Lopes, “A comparison of swarm intelligence algorithms for structural engineering optimization,” Int. J. for Numerical Methods in Engneering, Vol.91, No.5, pp. 666-684, 2012.
  16. [16]  C. Pilar, B. Francisco, A. Jose, et al., “Evolutionary algorithm characterization in real parameter optimization problems,” Applied Soft Computing, Vol.2, No.1, pp. 1902-1921, 2013.
  17. [17]  W. Meng, X. Han, and B. Honh, “Bee Evolutionary Genetic Algorithm,” Acta Electronica Sinica, Vol.34, No.7, pp. 1294-1300, 2006.
  18. [18]  L. Z. Xu and J. T. Yang, “Universal Operator of Genetic Operation and Image Restoration,” J. of Circuits and Systems, Vol.4, No.2, pp. 80-85, 1999.
  19. [19]  M. Mitchell, “An Introduction to Genetic Algorithms,” MIT Press, pp. 101-110, 1998.
  20. [20]  D. W. Wang, J. W. Wang, and H. Wang, “Intelligent Optimization Methods,” Higher Education Press, pp. 202-211, 2007.
  21. [21]  J. W. Wang and D. W. Wang, “Experiments and analysis on inertia weight in particle swarm optimization,” J. of Systems Engineering, Vol.20, No.1, pp. 194-198, 2005.
  22. [22]  J. Kennedy and R. Eberhart, “Swarm Intelligence,” Academic Press, pp. 112-119, 2001.
  23. [23]  X. Li, K. Tang, M. N. Omidvar, et al., “Benchmark functions for the CEC 2013 special session and competition on large-scale global optimization,” Gene, Vol.7, pp. 33, 2013.

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