Skip to main content

A New Bio-heuristic Hybrid Optimization for Constrained Continuous Problems

  • Chapter
  • First Online:
Transactions on Computational Science XXXVIII

Part of the book series: Lecture Notes in Computer Science ((TCOMPUTATSCIE,volume 12620))

Abstract

A novel bio-inspired evolutionary algorithm known as MoFAL is presented in this article. The proposed algorithm (MoFAL) is based on the hybrid amalgamation of two nature inspired methods based on Moth Flame Optimization and Ant Lion Optimizer algorithms. It is well known that elitism forms an important characteristic of evolutionary algorithms that allows them to maintain the best fitness(es) obtained at any stage of the optimization process. MoFal is bench-marked using a set of 23 classical benchmark functions employed to test different characteristics during its evolutionary computation process. Numerical experiments demonstrate that the solutions of the constrained optimization problems like Pressure Vessel and the Rolling Element Bearing designs found using our algorithm are highly accurate and their convergence is comparatively fast coupled with improved exploration, local optima avoidance and exploitation. The results clearly exhibit that MoFAL algorithm is capable of finding superior optimal designs for our case study problems that include diverse search spaces. Our algorithm is able to determine global solutions of constrained optimization problems more efficiently than traditional evolutionary algorithms, and also avoid the occurrence of premature phenomena during its convergence process.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 39.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 54.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Similar content being viewed by others

References

  • Black, P.: “Gray code”, from dictionary of algorithms and data structures, Paul E. Black (ed.). NIST, January 2005

    Google Scholar 

  • Elkhechafi, M., Hachimi, H., Elkettani, Y.: A new hybrid cuckoo search and firefly optimization. Monte Carlo Methods Appl., 2 (2018)

    Google Scholar 

  • Frank, K.: Effects of artificial night lighting on moths. In: Ecological Consequences of Artificial Night Lighting, pp. 305–344 (2006)

    Google Scholar 

  • Ganesan, K., Ponnambalam, S.G., Jawahar, N., Janardhanan, M.N.: An effective hybrid cuckoo search and genetic algorithm for constrained engineering design optimization. Eng. Optim. 46, 1331–1351 (2014)

    Article  MathSciNet  Google Scholar 

  • Gaston, K., Bennie, J., Davies, T., Hopkins, J.: The ecological impacts of nighttime light pollution: a mechanistic appraisal. Biol. Rev. Camb. Philos. Soc. 88, 912–927 (2013)

    Article  Google Scholar 

  • Grosan, C., Abraham, A.: Hybrid evolutionary algorithms: methodologies, architectures, and reviews. In: Abraham, A., Grosan, C., Ishibuchi, H. (eds.) Hybrid Evolutionary Algorithms. Studies in Computational Intelligence, vol. 75, pp. 1–17. Springer, Heidelberg (2007). https://doi.org/10.1007/978-3-540-73297-6_1

    Chapter  MATH  Google Scholar 

  • Ho, Y.C., Pepyne, D.L.: Simple explanation of the no free lunch theorem of optimization. Cybern. Syst. Anal. 38(2), 292–298 (2002). https://doi.org/10.1023/A:1016355715164

    Article  MathSciNet  MATH  Google Scholar 

  • Holland, J.H.: Genetic Algorithms, vol. 266 (1992)

    Google Scholar 

  • Hybrid Algorithms -Evolutionary: Hybrid algorithm – Wikipedia, the free encyclopedia (2020). https://en.wikipedia.org/wiki/Hybrid_algorithm. Accessed 29 Oct 2020

  • Jangir, P., Parmar, S., Jangir, N., Kumar, A., Trivedi, I., Bhoye, M.: Optimal power flow using an hybrid particle swarm optimizer with moth flame optimizer. Appl. Soft Comput. (2016)

    Google Scholar 

  • Judith, E.: Perspectives on Animal Behavior (2010)

    Google Scholar 

  • Kennedy, J., Eberhart, R.: Particle swarm optimization, pp. 1942–1948 (1995)

    Google Scholar 

  • Köse, U.: An ant-lion optimizer-trained artificial neural network system for chaotic electroencephalogram (EEG) prediction. Appl. Sci. 8, 1613 (2018)

    Article  Google Scholar 

  • Li, Z., Zhou, Y.Q., Zhang, S., Song, J.: Lévy-flight moth-flame algorithm for function optimization and engineering design problems. Math. Probl. Eng. 2016, 1–22 (2016)

    Google Scholar 

  • Mageshkumar, C., Karthik, S., Arunachalam, V.P.: Hybrid metaheuristic algorithm for improving the efficiency of data clustering. Cluster Comput. 22(1), 435–442 (2018). https://doi.org/10.1007/s10586-018-2242-8

    Article  Google Scholar 

  • Majhi, S., Biswal, S.: Optimal cluster analysis using hybrid k-means and ant lion optimizer. Karbala Int. J. Mod. Sci. 4, 347–360 (2018)

    Article  Google Scholar 

  • Mirjalili, S.: The ant lion optimizer. Adv. Eng. Softw. 83(C), 80–98 (2015a). https://doi.org/10.1016/j.advengsoft.2015.01.010

  • Mirjalili, S.: Moth-flame optimization algorithm. Know.-Based Syst. 89(C), 228–249 (2015b). https://doi.org/10.1016/j.knosys.2015.07.006

  • Mirjalili, S.: Matlab central file exchange. Retrieved ant lion optimizer (alo) datasets, October 2020a. https://www.mathworks.com/matlabcentral/fileexchange/49920-ant-lion-optimizer-alo

  • Mirjalili, S.: Matlab central file exchange. Retrieved moth-flame optimization (mfo) datasets, October 2020b. https://www.mathworks.com/matlabcentral/fileexchange/52269-moth-flame-optimization-mfo-algorithm

  • Parvathi, P., Rajendran, R.: A hybrid FCM-ALO based technique for image segmentation, pp. 342–345, October 2016

    Google Scholar 

  • Pisula, W.: Curiosity and Information Seeking in Animal and Human Behavior, January 2009

    Google Scholar 

  • Sarbazfard, S., Jafarian, A.: A hybrid algorithm based on firefly algorithm and differential evolution for global optimization. Int. J. Adv. Comput. Sci. Appl. 7, 95–106 (2016)

    Google Scholar 

  • Sayed, G.I., Hassanien, A.E.: A hybrid SA-MFO algorithm for function optimization and engineering design problems. Complex Intell. Syst. 4(3), 195–212 (2018). https://doi.org/10.1007/s40747-018-0066-z

    Article  Google Scholar 

  • Scharf, I., Ovadia, O.: Factors influencing site abandonment and site selection in a sit-and-wait predator: a review of pit-building antlion larvae. J. Insect Behav. 19(2), 197–218 (2006). https://doi.org/10.1007/s10905-006-9017-4

    Article  Google Scholar 

  • Scharf, I., Subach, A., Ovadia, O.: Foraging behaviour and habitat selection in pit-building antlion larvae in constant light or dark conditions. Anim. Behav. 76, 2049–2057 (2008)

    Article  Google Scholar 

  • Stojanovic, I.: Application of heuristic and metaheuristic algorithms in solving constrained weber problem with feasible region bounded by arcs, April 2017

    Google Scholar 

  • Storn, R., Price, K.: Differential evolution - a simple and efficient heuristic for global optimization over continuous spaces. J. Global Optim. 11(4), 341–359 (1997). https://doi.org/10.1023/A:1008202821328

    Article  MathSciNet  MATH  Google Scholar 

  • Ullah, I., Hussain, S.: An efficient energy management in office using bio-inspired energy optimization algorithms. Processes 7 (2019)

    Google Scholar 

  • Wahid, F., Ghazali, R., Shah, H.: An improved hybrid firefly algorithm for solving optimization problems. In: Ghazali, R., Deris, M.M., Nawi, N.M., Abawajy, J.H. (eds.) SCDM 2018. AISC, vol. 700, pp. 14–23. Springer, Cham (2018). https://doi.org/10.1007/978-3-319-72550-5_2

    Chapter  Google Scholar 

  • Wang, F.: Hybrid optimization algorithm of PSO and cuckoo search. In: 2011 2nd International Conference on Artificial Intelligence, Management Science and Electronic Commerce (AIMSEC), pp. 1172–1175, August 2011

    Google Scholar 

  • Wilcoxon, F., Katti, S., Wilcox, R.A.: Critical values and probability levels for the Wilcoxon rank sum test and the Wilcoxon signed rank test, Pearl River, N.Y. American Cyanamid, January 1963

    Google Scholar 

  • Xia, X., et al.: A hybrid optimizer based on firefly algorithm and particle swarm optimization algorithm. J. Comput. Sci. 26 (2017)

    Google Scholar 

  • Xu, H., Liu, X., Su, J.: An improved grey wolf optimizer algorithm integrated with cuckoo search, vol. 1, pp. 490–493 (2017)

    Google Scholar 

  • Yang, X.-S.: Flower pollination algorithm for global optimization. In: Durand-Lose, J., Jonoska, N. (eds.) UCNC 2012. LNCS, vol. 7445, pp. 240–249. Springer, Heidelberg (2012). https://doi.org/10.1007/978-3-642-32894-7_27

    Chapter  Google Scholar 

Download references

Acknowledgments

The work in this article is supported by the Optimization Problems Research and Application Laboratory (OPR-AL), Ryerson University and Natural Sciences and Engineering Research Council of Canada (NSERC). Also, we thank for the open source datasets provided by the original algorithm formulators at Mirjalili (2020a), Mirjalili (2020b) which assisted in our research experiments.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Reza Sedaghat .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2021 Springer-Verlag GmbH Germany, part of Springer Nature

About this chapter

Check for updates. Verify currency and authenticity via CrossMark

Cite this chapter

Siddavaatam, P., Sedaghat, R. (2021). A New Bio-heuristic Hybrid Optimization for Constrained Continuous Problems. In: Gavrilova, M.L., Tan, C.K. (eds) Transactions on Computational Science XXXVIII. Lecture Notes in Computer Science(), vol 12620. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-662-63170-6_5

Download citation

  • DOI: https://doi.org/10.1007/978-3-662-63170-6_5

  • Published:

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-662-63169-0

  • Online ISBN: 978-3-662-63170-6

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics