Dolphin swarm algorithm

Abstract

By adopting the distributed problem-solving strategy, swarm intelligence algorithms have been successfully applied to many optimization problems that are difficult to deal with using traditional methods. At present, there are many well-implemented algorithms, such as particle swarm optimization, genetic algorithm, artificial bee colony algorithm, and ant colony optimization. These algorithms have already shown favorable performances. However, with the objects becoming increasingly complex, it is becoming gradually more difficult for these algorithms to meet human’s demand in terms of accuracy and time. Designing a new algorithm to seek better solutions for optimization problems is becoming increasingly essential. Dolphins have many noteworthy biological characteristics and living habits such as echolocation, information exchanges, cooperation, and division of labor. Combining these biological characteristics and living habits with swarm intelligence and bringing them into optimization problems, we propose a brand new algorithm named the ‘dolphin swarm algorithm’ in this paper. We also provide the definitions of the algorithm and specific descriptions of the four pivotal phases in the algorithm, which are the search phase, call phase, reception phase, and predation phase. Ten benchmark functions with different properties are tested using the dolphin swarm algorithm, particle swarm optimization, genetic algorithm, and artificial bee colony algorithm. The convergence rates and benchmark function results of these four algorithms are compared to testify the effect of the dolphin swarm algorithm. The results show that in most cases, the dolphin swarm algorithm performs better. The dolphin swarm algorithm possesses some great features, such as first-slow-then-fast convergence, periodic convergence, local-optimum-free, and no specific demand on benchmark functions. Moreover, the dolphin swarm algorithm is particularly appropriate to optimization problems, with more calls of fitness functions and fewer individuals.

Keywords

Swarm intelligence / Bio-inspired algorithm / Dolphin / Optimization

References

[1] Bonabeau, E., Dorigo, M., Theraulaz, G., 1999. Swarm Intelligence: from Natural to Artificial Systems. Oxford University Press.

[2] Cura, T., 2012. A particle swarm optimization approach to clustering. Expert Syst. Appl., 39(1):1582–1588. http://dx.doi.org/10.1016/j.eswa.2011.07.123

[3] Dorigo, M., Birattari, M., 2010. Ant colony optimization. In: Sammut, C., Webb, G.I. (Eds.), Encyclopedia of Machine Learning. Springer, p.36–39. http://dx.doi.org/10.1007/978-0-387-30164-8_22

[4] Dorigo, M., Maniezzo, V., Colorni, A., 1996. Ant system: optimization by a colony of cooperating agents. IEEE Trans. Syst. Man Cybern. B, 26(1):29–41. http://dx.doi.org/10.1109/3477.484436

[5] Ducatelle, F., di Caro, G.A., Gambardella, L.M., 2010. Principles and applications of swarm intelligence for adaptive routing in telecommunications networks. Swarm Intell., 4(3):173–198. http://dx.doi.org/10.1007/s11721-010-0040-x

[6] Eberhart, R.C., Kennedy, J., 1995. A new optimizer using particle swarm theory. Proc. 6th Int. Symp. on Micro Machine and Human Science, p.39–43. http://dx.doi.org/10.1109/mhs.1995.494215

[7] Eberhart, R.C., Shi, Y.H., 2001. Particle swarm optimization: developments, applications and resources. Proc. Congress on Evolutionary Computation, p.81–86. http://dx.doi.org/10.1109/CEC.2001.934374

[8] Garnier, S., Gautrais, J., Theraulaz, G., 2007. The biological principles of swarm intelligence. Swarm Intell., 1(1):3–31. http://dx.doi.org/10.1007/s11721-007-0004-y

[9] Karaboga, D., 2005. An Idea Based on Honey Bee Swarm for Numerical Optimization. Technical Report-TR06, Erciyes University, Turkey.

[10] Karaboga, D., Basturk, B., 2007. A powerful and efficient algorithm for numerical function optimization: artificial bee colony (ABC) algorithm. J. Glob. Optim., 39(3):459–471. http://dx.doi.org/10.1007/s10898-007-9149-x

[11] Karaboga, D., Gorkemli, B., Ozturk, C., , 2014. A comprehensive survey: artificial bee colony (ABC) algorithm and applications. Artif. Intell. Rev., 42(1):21–57. http://dx.doi.org/10.1007/s10462-012-9328-0

[12] Kennedy, J., 2010. Particle swarm optimization. In: Sammut, C., Webb, G.I. (Eds.), Encyclopedia of Machine Learning. Springer, p.760–766. http://dx.doi.org/10.1007/978-0-387-30164-8_630

[13] Mitchell, M., 1998. An Introduction to Genetic Algorithms. MIT Press.

[14] Mohan, B.C., Baskaran, R., 2012. A survey: ant colony optimization based recent research and implementation on several engineering domains. Expert Syst. Appl., 39(4): 4618–4627. http://dx.doi.org/10.1016/j.eswa.2011.09.076

[15] Parpinelli, R.S., Lopes, H.S., 2011. New inspirations in swarm intelligence: a survey. Int. J. Bio-inspired Comput., 3(1):1–16. http://dx.doi.org/10.1504/IJBIC.2011.038700

[16] Poli, R., Kennedy, J., Blackwell, T., 2007. Particle swarm optimization. Swarm Intell., 1(1):33–57. http://dx.doi.org/10.1007/s11721-007-0002-0

[17] Saleem, M., di Caro, G.A., Farooq, M., 2011. Swarm intelligence based routing protocol for wireless sensor networks: survey and future directions. Inform. Sci., 181(20):4597–4624. http://dx.doi.org/10.1016/j.ins.2010.07.005

[18] Whitley, D., 1994. A genetic algorithm tutorial. Stat. Comput., 4(2):65–85. http://dx.doi.org/10.1007/BF00175354

[19] Yao, X., Liu, Y., Lin, G.M., 1999. Evolutionary programming made faster. IEEE Trans. Evol. Comput., 3(2):82–102. http://dx.doi.org/10.1109/4235.771163

RIGHTS & PERMISSIONS

2016 Zhejiang University and Springer-Verlag Berlin Heidelberg