Spider monkey optimization algorithm for constrained optimization problems
- 303 Downloads
In this paper, a modified version of spider monkey optimization (SMO) algorithm for solving constrained optimization problems has been proposed. To the best of author’s knowledge, this is the first attempt to develop a version of SMO which can solve constrained continuous optimization problems by using the Deb’s technique for handling constraints. The proposed algorithm is named constrained spider monkey optimization (CSMO) algorithm. The performance of CSMO is investigated on the well-defined constrained optimization problems of CEC2006 and CEC2010 benchmark sets. The results of the proposed algorithm are compared with constrained versions of particle swarm optimization, artificial bee colony and differential evolution. Outcome of the experiment and the discussion of results demonstrate that CSMO handles the global optimization task very well for constrained optimization problems and shows better performance in comparison with compared algorithms. Such an outcome will be an encouragement for the research community to further explore the potential of SMO in solving benchmarks as well as real-world problems, which are often constrained in nature.
KeywordsSpider monkey optimization Constrained optimization CEC2006 CEC2010 Deb’s technique
The first author would like to thank Ministry of Human Resource Development, Govt. of India, for funding this research under the Grant No. MHR-02-23-200-44.
Compliance with ethical standards
Conflict of interest
The authors declare that they have no conflict of interest.
This article does not contain any studies with human participants or animals performed by any of the authors.
- Dorigo M (1992) Optimization, learning and natural algorithms, Ph.D. thesis, Politecnico di Milano, ItalyGoogle Scholar
- Elsayed SM, Sarker RA, Essam DL (2011) GA with a new multi-parent crossover for constrained optimization. In: IEEE Congress on Evolutionary Computation, CEC’2011. IEEE Press, New Orleans, USA 2011, pp 857–864Google Scholar
- Gupta K, Deep K (2016) Investigation of suitable perturbation rate scheme for spider monkey optimization algorithm. In: Proceedings of fifth international conference on soft computing for problem solving, vol. 437, Springer, Singapore, pp 839–850Google Scholar
- Gupta K, Deep K (2016) Tournament Selection Based Probability Scheme in Spider Monkey Optimization Algorithm: In Harmony Search Algorithm, vol. 382, Springer, Berlin, pp 239–250Google Scholar
- Gupta K, Deep K, Bansal JC (2016) Improving the local search ability of spider monkey optimization algorithm using quadratic approximation for unconstrained optimization. Comput Intell. doi: 10.1111/coin.12081
- Holland JH (1975) Adaptation in natural and artificial systems. University of Michigan Press, AnnArborGoogle Scholar
- Karaboga D (2005) An idea based on honeybee swarm for numerical optimization. Technical Report, TR06, Erciyes University, Engineering Faculty, Computer Engineering DepartmentGoogle Scholar
- Kennedy J, Eberhart R (1995) Particle swarm optimization. In: Proceedings, IEEE International Conference, vol. 4, pp. 1942–1948Google Scholar
- Liang JJ et al (2006) Problem definitions and evaluation criteria for the CEC 2006 special session on constrained real-parameter optimization. In: Technical Report. Nanyang Technological University, SingaporeGoogle Scholar
- Mallipeddi R, Suganthan PN (2010) Problem definitions and evaluation criteria for the CEC 2010 competition on constrained real-parameter optimization. Nanyang Technological University, SingaporeGoogle Scholar
- Mezura-Montes E, Coello CAC, Tun-Morales EI (2004) Simple feasibility rules and differential evolution for constrained optimization. In: IMICAI’2004, LNAI 2972, pp. 707–716Google Scholar
- Mezura-Montes E, Hernández-Ocaña B (2009) Modified bacterial foraging optimization for engineering design. In: Dagli CH et al (eds) Proceedings of the artificial neural networks in engineering conference, ANNIE’2009, in: Intelligent Engineering Systems Through Artificial Neural Networks, vol 19., ASME PressSt. Louis, MO, USA, pp 357–364Google Scholar
- Mezura-Montes E, Velázquez-Reyes J, Coello CAC (2005) Promising infeasibility and multiple offspring incorporated to differential evolution for constrained optimization. In: Proceedings of the genetic and evolutionary computation conference, GECCO’2005, vol 1, Washington, DC, USA, ACM Press, New York, ISBN: 1-59593-010-8, pp 225–232Google Scholar
- Mezura-Montes E, Velázquez-Reyes J, Coello CAC (2006) Modified differential evolution for constrained optimization. In: IEEE Congress on Evolutionary Computation, CEC’2006. IEEE, Vancouver, BC, Canada 2006, pp 332–339Google Scholar
- Muñoz-Zavala AE, Aguirre AH, Diharce ERV (2005) Constrained optimization via particle evolutionary swarm optimization algorithm (PESO). In: Proceedings of the genetic and evolutionary computation conference, GECCO’2005, vol 1, Washington DC, USA, ACM Press, New York, ISBN:1-59593-010-8, pp 209–216Google Scholar
- Muñoz-Zavala AE, et al (2006) PESO+ for constrained optimization. In: IEEE international congress on evolutionary computation, pp 231-238Google Scholar
- Sun C-L, Zeng J-C, Pan JS (2009) A particle swarm optimization with feasibility based rules for mixed-variable optimization problems. In: Ninth international conference on hybrid intelligent systems, 2009, HIS’09, IEEE Computer Society Press, Shenyang, China, pp 543–547Google Scholar
- Sun C-L, Zeng J-C, Pan J-S (2009) An improved particle swarm optimization with Feasibility-based rules for mixed-variable optimization problems. In: Fourth international conference on innovative computing, information and control, IEEE Computer Society Press, 2009, pp 897–903Google Scholar
- Toscano-Pulido G, Coello CAC (2004) A constraint-handling mechanism for particle swarm optimization. In: Proceedings of the congress on evolutionary computation 2004, CEC’ 2004, vol 2, Portland, Oregon, USA, IEEE Service Center, Piscataway, New Jersey, pp 1396–1403Google Scholar
- Zielinski K, Laur R (2006) Constrained single-objective optimization using particle swarm optimization. In: IEEE congress on evolutionary computation, CEC’2006. IEEE, Vancouver, BC, Canada 2006, pp 443–450Google Scholar