Spider Monkey Optimization

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Spider Monkey Optimization (SMO) is a recent addition in the field of nature inspired optimization algorithms developed by Bansal et al.[1] SMO is based on the intelligent foraging behavior of spider monkeys. SMO can be broadly classified as a computational intelligence technique for global optimization.

Introduction

The name swarm is used for an accumulation of creatures such as ants, fish, birds, termites and honey bees which behave collectively. The definition given by Bonabeau for the swarm intelligence is “any attempt to design algorithms or distributed problem-solving devices inspired by the collective behavior of social insect colonies and other animal societies” [3]. Swarm intelligence is a meta-heuristic approach in the field of nature inspired techniques that is used to solve optimization problems. It is based on the collective behavior of social creatures. Social creatures utilize their ability of social learning and adaptation to solve complex tasks. Researchers have analyzed such behaviors and designed algorithms that can be used to solve nonlinear, non-convex or combinatorial optimization problems in many science and engineering domains. Previous research [7,17,28,39] have shown that algorithms based on Swarm Intelligence have great potential to find a near optimal solution of real world optimization problem. The algorithms that have been emerged in recent years are Ant Colony Optimization (ACO) [7], Particle Swarm Optimization (PSO) [17], Bacterial Foraging Optimization (BFO) [26], Artificial Bee Colony Optimization (ABC) [14] etc.

Background

Before, designing a new swarm intelligence based algorithm, it must understand that whether a behavior is swarm intelligence or not. Two approaches Divison of Labor and Self-Organization are the necessary and sufficient conditions for obtaining intelligent swarming behaviors mentioned by Karaboga et.al.

1.Self-organization: It is an important feature of swarm, in this interection among low level component without a central authority or external element enforcing it through planning. that is the globally coherent pattern appears from the local interection of the component that build up the structure. Because of no element is a central co-ordinator and all the element acts at the same time as Distributed thus the organization achieved in parallel.

Bonabeau et al.have defined the following four important characteristics on which self-organization is based:

Positive feedback:is an information extracted from the output of a system and reapplied to the input to promotes the creations of convenient structures. In the field of swarm intelligence positive feedback provides diversity and accelerate the system to new stable state.

Negative feedback: compensates the effect of positive feedback and helps to stabilize the collective pattern.
Fluctuations: are the rate or magnitude of random changes in the system. Randomness is often crucial for efflorescent structures since it allows the findings of new solutions. In foraging process, it helps to get-ride of stagnation.

Multiple interactions: provide the way of learning from the individuals within a society and thus enhance the combined intelligence of the swarm.

2. Division of labor: is a cooperative labor in specific, circumscribed tasks and like roles. In a group, there are various tasks, which are performed simultaneously by specialized individuals. Simultaneous task performance by cooperating specialized individuals is believed to be more efficient than the sequential task performance by unspecialized individuals [5,13,24].

This paper proposes a new swarm intelligence algorithm based on the foraging behavior of spider monkeys. The for- aging behavior of spider monkeys shows that these monkeys fall in the category of fission–fusion social structure (FFSS) based animals. Thus the proposed optimization algorithm which is based on foraging behavior of spider monkeys is explained better in terms of FFSS. Further, the proposed strategy is tested on various benchmark and engineering optimization test problems.

The rest of the paper is organized as follows: Sect. 2 describes the foraging behavior and social structure of spider monkeys. In Sect. 3, first, the foraging behavior is critically evaluated to be a swarm intelligent behavior over the necessary and sufficient conditions of swarm intelligence and then Spider Monkey Optimization algorithm is proposed. A detail discussion about the proposed strategy is presented in Sect. 4. In Sect. 5, performance of the proposed strategy is analyzed and compared with four state-of-the-art algorithms, namely DE, PSO, ABC and CMA-ES. Finally, in Sect. 6, paper is concluded.

References

  1. [1]

[1] [2][3] [4] [5] [6] [7] [8] [9] [10] [11] [12] [13] [14] [15] [16] [17] [18] [19] [20] [21] [22][23] [24] [25] [26][27] [28] [29][30][31][32][33][34][35][36][37][38][39][40]

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  1. Bansal, J.; Sharma, H.; Jadon, S. & Clerc, M. Spider Monkey Optimization algorithm for numerical optimization Memetic Computing, Springer Berlin Heidelberg, 2014, 6, 31-47
  2. Ali MM, Khompatraporn C, Zabinsky ZB (2005) A numerical evaluation of several stochastic algorithms on selected continuous global optimization test problems. J. Global Optim. 31(4):635–672
  3. Angeline P (1998) Evolutionary optimization versus particleswarm optimization: philosophy and performance differences. In:Evolutionary programming VII. Springer, Berlin, pp 601–610
  4. Bonabeau E, Dorigo M, Theraulaz G (1999) Swarm intelligence:from natural to artificial systems. Oxford University Press, NewYork
  5. Clerc M (2012) A method to improve standard PSO. http://clerc.maurice.free.fr/pso/Design_efficient_PSO.pdf. Retrieved on Jan 2012
  6. De Castro LN, Von Zuben FJ (1999) Artificial immune systems:Part I-basic theory and applications. Universidade Estadual de Campinas, Dezembro de, Tech. Rep
  7. Thakur M. Deep K (2007) A new crossover operator for real coded genetic algorithms. Appl Math Comput 188(1):895911
  8. Dorigo M, Stützle T (2004) Ant colony optimization. The MIT Press, Cambridge
  9. Gamperle R, Muller SD, Koumoutsakos A (2002) A parameter study for differential evolution. Adv Intell Syst Fuzzy Syst Evol Comput 10:293–298
  10. Goldberg DE (1989) Genetic algorithms in search, optimization,and machine learning. Addison-Wesley Professional, Upper Saddle River
  11. Hansen N (2006) The cma evolution strategy: a comparing review.In: Towards a new evolutionary computation. Springer, Heidelberg, pp 75–102
  12. Hansen N, Ostermeier A (1996) Adapting arbitrary normal mutation distributions in evolution strategies: the covariance matrix adaptation. In: Proceedings of IEEE international conference on evolutionary computation, pp 312–317. IEEE
  13. Hofmann K, Whiteson S, de Rijke M (2011) Balancing exploration and exploitation in learning to rank online. Adv Inform Retr 5:251– 263
  14. Jeanne RL (1986) The evolution of the organization of work in social insects. Monitore Zoologico Italiano 20(2):119–133
  15. Karaboga D (2005) An idea based on honey bee swarm for numerical optimization. Techn. Rep. TR06. Erciyes University Press, Erciyes
  16. Karaboga D, Akay B (2009) A comparative study of artificial beecolony algorithm. Appl Math Comput 214(1):108–132
  17. Karaboga D, Akay B (2011) A modified artificial bee colony (ABC)algorithm for constrained optimization problems. Appl Soft Com- put 11(3):3021–3031
  18. Kennedy J, Eberhart R (1995) Particle swarm optimization. In: Proceedings of the IEEE international conference on neural networks, 1995, vol 4, pp 1942–1948. IEEE
  19. Lampinen J, Zelinka I (2000) On stagnation of the differential evolution algorithm. In: Proceedings of MENDEL, Citeseer, pp 76–83
  20. Mann HB, Whitney DR (1947) On a test of whether one of two random variables is stochastically larger than the other. Annals Math Stat 18(1):50–60
  21. Mezura-Montes E, Velázquez-Reyes J, Coello CA (2006) A comparative study of differential evolution variants for global opti- mization. In: Proceedings of the 8th annual conference on Genetic and evolutionary computation. ACM Press, New York, pp 485– 492
  22. Milano M, Koumoutsakos P, Schmidhuber J (2004) Self-organizing nets for optimization. IEEE Trans Neural Netw 15(3):758–765
  23. Milton K (1993) Diet and social organization of a free-ranging spider monkey population: the development of species-typical behav- ior in the absence of adults. In: Juvenile primates: life history,development, and behavior. Oxford University Press, Oxford, pp 173–181
  24. Norconk MA, Kinzey WG (1994) Challenge of neotropical frugivory: travel patterns of spider monkeys and bearded sakis. Am J Primatol 34(2):171–183
  25. Oster GF, Wilson EO (1979) Caste and ecology in the social insects.Princeton Univ ersity Press, Princeton
  26. Passino KM (2002) Biomimicry of bacterial foraging for distributed optimization and control. IEEE Control Syst Mag 22(3):52–67
  27. Passino KM (2010) Bacterial foraging optimization. Int J Swarm Intell Res (IJSIR) 1(1):1–16
  28. Price KV (1996) Differential evolution: a fast and simple numerical optimizer. In: Fuzzy information processing society, 1996. NAFIPS. 1996 Biennial conference of the North American, pp524–527. IEEE
  29. Price KV, Storn RM, Lampinen JA (2005) Differential evolution:a practical approach to global optimization. Springer, Berlin
  30. Rahnamayan S, Tizhoosh HR, Salama MMA (2008) Opposition-based differential evolution. IEEE Trans Evol Comput 12(1):64–79
  31. amos-Fernandez G (2001) Patterns of association, feeding competition and vocal communication in spider monkeys, Ateles geof- froyi. Dissertations, University of Pennsylvania. http://repository. upenn.edu/dissertations/AAI3003685. 1 Jan 2001
  32. Sartore J (2011) Spider monkey images. http://animals.national geographic.com/animals/mammals/spider-monkey. Retrieved on 21 Decmber 2011
  33. Sharma H, Bansal JC, Arya KV (2012) Opposition based lévy flight artificial bee colony. Memet Comput 5(3):213–227
  34. Shi Y, Eberhart R (1998) Parameter selection in particle swarm optimization. In: Evolutionary programming VII. Springer, Hei- delberg, pp 591–600
  35. Simmen B, Sabatier D (1996) Diets of some french guianan primates: food composition and food choices. Int J Primatol 17(5):661–693
  36. Storn R, Price K (1997) Differential evolution-a simple and effi- cient adaptive scheme for global optimization over continuous spaces. J Global Optim 11:341–359
  37. Suganthan PN, Hansen N, Liang JJ, Deb K, Chen YP, Auger A, Tiwari S (2005) Problem definitions and evaluation criteria for the CEC 2005 special session on real-parameter optimization. Kan- GAL Report
  38. Symington MMF (1990) Fission–fusion social organization inate- les andpan. Int J Primatol 11(1):47–61
  39. 38. van Roosmalen MGM (1985) Instituto Nacional de Pesquisas da Amazônia. Habitat preferences, diet, feeding strategy and social organization of the black spider monkey (ateles paniscus paniscus linnaeus 1758) in surinam. Wageningen : Roosmalen
  40. Vesterstrom J, Thomsen R (2004) A comparative study of differen- tial evolution, particle swarm optimization, and evolutionary algo- rithms on numerical benchmark problems. In: Congress on evolu- tionary computation, 2004. CEC2004., vol 2, pp 1980–1987. IEEE