Skip to main content
SpringerLink
Log in

Chaotic lightning search algorithm

  • Methodologies and Application
  • Published:
Soft Computing Aims and scope Submit manuscript

Abstract

Metaheuristics have proven their efficiency in treating complex optimization problems. Generally, they produce good results quite close to optimal despite some weaknesses such as premature convergence and stagnation in the local optima. However, some techniques are used to improve the obtained results, one of them is the adoption of chaos theory. Including chaotic sequences in metaheuristics has proven its efficiency in previous studies by improving the performance and quality of the results obtained. In this study, we propose an improvement of the metaheuristic lightning search algorithm (LSA) by using chaos theory. In fact, the idea is to replace the values of random variables with a chaotic sequences generator. To prove the success of the metaheuristic—chaos theory association, we tested five chaotic version of lightning search algorithm on a benchmark of seven functions. Experimental results show that sine or singer map are the best choices to improve the efficiency of LSA, in particular with the lead projectile update.

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

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8

Similar content being viewed by others

References

  • Alatas B (2010a) Chaotic bee colony algorithms for global numerical optimization. Expert Syst Appl 37(8):5682–5687

    Article  Google Scholar 

  • Alatas B (2010b) Chaotic harmony search algorithms. Appl Math Comput 216(9):2687–2699

    Article  Google Scholar 

  • Alatas B, Akin E, Ozer AB (2009) Chaos embedded particle swarm optimization algorithms. Chaos Solitons Fractals 40(4):1715–1734

    Article  MathSciNet  Google Scholar 

  • Aljanad A, Mohamed A, Shareef H, Khatib T (2018) A novel method for optimal placement of vehicle-to-grid charging stations in distribution power system using a quantum binary lightning search algorithm. Sustain Cities Soc 38:174–183

    Article  Google Scholar 

  • Arora S, Singh S (2017) An improved butterfly optimization algorithm with chaos. J Intell Fuzzy Syst 32(1):1079–1088

    Article  Google Scholar 

  • Blum C, Puchinger J, Raidl GR, Roli A (2011) Hybrid metaheuristics in combinatorial optimization: a survey. Appl Soft Comput 11(6):4135–4151

    Article  Google Scholar 

  • Chen H, Zhang Q, Luo J, Xu Y, Zhang X (2020) An enhanced bacterial foraging optimization and its application for training kernel extreme learning machine. Appl Soft Comput 86:105884

    Article  Google Scholar 

  • Dorigo M, Di Caro G (1999) Ant colony optimization: a new meta-heuristic. In: Proceedings of the 1999 congress on evolutionary computation-CEC99 (Cat. No. 99TH8406), IEEE, vol 2, pp 1470–1477

  • Eberhart R, Kennedy J (1995) Particle swarm optimization. In: Proceedings of the IEEE international conference on neural networks, Citeseer, vol 4, pp 1942–1948

  • Gandomi AH, Alavi AH (2012) Krill herd: a new bio-inspired optimization algorithm. Commun Nonlinear Sci Numer Simul 17(12):4831–4845

    Article  MathSciNet  Google Scholar 

  • Gandomi AH, Yang XS (2014) Chaotic bat algorithm. J Comput Sci 5(2):224–232

    Article  MathSciNet  Google Scholar 

  • Gandomi AH, Yang XS, Talatahari S, Alavi AH (2013) Firefly algorithm with chaos. Commun Nonlinear Sci Numer Simul 18(1):89–98

    Article  MathSciNet  Google Scholar 

  • Gen M, Cheng R (1996) Genetic algorithms and manufacturing systems design. Wiley, New York

    Book  Google Scholar 

  • Glover F, Laguna M (1998) Tabu search. Handbook of combinatorial optimization. Springer, New York, pp 2093–2229

    Chapter  Google Scholar 

  • Hannan MA, Abd Ali J, Hussain A, Hasim FH, Amirulddin UAU, Uddin MN, Blaabjerg F (2017) A quantum lightning search algorithm-based fuzzy speed controller for induction motor drive. IEEE Access 6:1214–1223

    Article  Google Scholar 

  • Hannan MA, Ali JA, Mohamed A, Amirulddin UAU, Tan NML, Uddin MN (2018) Quantum-behaved lightning search algorithm to improve indirect field-oriented fuzzy-PI control for IM drive. IEEE Trans Ind Appl 54(4):3793–3805

    Article  Google Scholar 

  • He S, Prempain E, Wu Q (2004) An improved particle swarm optimizer for mechanical design optimization problems. Eng Optim 36(5):585–605

    Article  MathSciNet  Google Scholar 

  • Heidari AA, Mirjalili S, Faris H, Aljarah I, Mafarja M, Chen H (2019) Harris Hawks optimization: algorithm and applications. Fut Generat Comput Syst 97:849–872

    Article  Google Scholar 

  • Ho YC, Pepyne DL (2002) Simple explanation of the no-free-lunch theorem and its implications. J Optim Theory Appl 115(3):549–570

    Article  MathSciNet  Google Scholar 

  • Holland JH (1992) Genetic algorithms. Sci Am 267(1):66–73

    Article  Google Scholar 

  • Islam MM, Shareef H, Mohamed A, Wahyudie A (2017) A binary variant of lightning search algorithm: BLSA. Soft Comput 21(11):2971–2990

    Article  Google Scholar 

  • Lim A, Rodrigues B, Zhang X (2004) Metaheuristics with local search techniques for retail shelf-space optimization. Manag Sci 50(1):117–131

    Article  Google Scholar 

  • Lourenço HR, Martin OC, Stützle T (2003) Iterated local search. Handbook of metaheuristics. Springer, New York, pp 320–353

    Chapter  Google Scholar 

  • Mirjalili S, Mirjalili SM, Lewis A (2014) Grey wolf optimizer. Adv Eng Softw 69:46–61

    Article  Google Scholar 

  • Mitić M, Vuković N, Petrović M, Miljković Z (2015) Chaotic fruit fly optimization algorithm. Knowl Based Syst 89:446–458

    Article  Google Scholar 

  • Murata T, Ishibuchi H (1994) Performance evaluation of genetic algorithms for flowshop scheduling problems. In: Proceedings of the first IEEE conference on evolutionary computation, IEEE World Congress on Computational Intelligence, IEEE, pp 812–817

  • Pacheco TM, Gonçalves LB, Ströele V, Soares SSR (2018) An ant colony optimization for automatic data clustering problem. In: 2018 IEEE congress on evolutionary computation (CEC), IEEE, pp 1–8

  • Paulinas M, Ušinskas A (2007) A survey of genetic algorithms applications for image enhancement and segmentation. Inf Technol control 36(3):278–284

    Google Scholar 

  • Saremi S, Mirjalili SM, Mirjalili S (2014) Chaotic krill herd optimization algorithm. Proc Technol 12:180–185

    Article  Google Scholar 

  • Shareef H, Ibrahim AA, Mutlag AH (2015) Lightning search algorithm. Appl Soft Comput 36:315–333

    Article  Google Scholar 

  • Thietart RA, Forgues B (1995) Chaos theory and organization. Organ Sci 6(1):19–31

    Article  Google Scholar 

  • Van Laarhoven PJ, Aarts EH (1987) Simulated annealing. In: Simulated annealing: theory and applications, Springer, New York, pp 7–15

  • Vignaux GA, Michalewicz Z (1991) A genetic algorithm for the linear transportation problem. IEEE Trans Syst Man Cybernet 21(2):445–452

    Article  MathSciNet  Google Scholar 

  • Wang GG, Deb S, Gandomi AH, Zhang Z, Alavi AH (2016) Chaotic cuckoo search. Soft Comput 20(9):3349–3362

    Article  Google Scholar 

  • Yang XS (2009a) Firefly algorithms for multimodal optimization. In: International symposium on stochastic algorithms, Springer, New York, pp 169–178

  • Yang XS (2009b) Harmony search as a metaheuristic algorithm. In: Music-inspired harmony search algorithm, Springer, New York, pp 1–14

  • Yang XS (2010) A new metaheuristic bat-inspired algorithm. In: Nature inspired cooperative strategies for optimization (NICSO 2010), Springer, New York, pp 65–74

  • Yang XS, Deb S (2009) Cuckoo search via lévy flights. In: 2009 World Congress on Nature & Biologically Inspired Computing (NaBIC), IEEE, pp 210–214

  • Yang XS, Ting T, Karamanoglu M (2013) Random walks, Lévy flights, markov chains and metaheuristic optimization. In: Future information communication technology and applications, Springer, New York, pp 1055–1064

  • Yu H, Yu Y, Liu Y, Wang Y, Gao S (2016) Chaotic grey wolf optimization. In: 2016 international conference on progress in informatics and computing (PIC), IEEE, pp 103–113

  • Zhang X, Feng T (2018) Chaotic bean optimization algorithm. Soft Comput 22(1):67–77

    Article  Google Scholar 

  • Zhou Y, Zhou Y, Luo Q, Abdel-Basset M (2017) A simplex method-based social spider optimization algorithm for clustering analysis. Eng Appl Artif Intell 64:67–82

    Article  Google Scholar 

  • Zhou Y, Miao F, Luo Q (2019a) Symbiotic organisms search algorithm for optimal evolutionary controller tuning of fractional fuzzy controllers. Appl Soft Comput 77:497–508

    Article  Google Scholar 

  • Zhou Y, Wu H, Luo Q, Abdel-Baset M (2019b) Automatic data clustering using nature-inspired symbiotic organism search algorithm. Knowl Based Syst 163:546–557

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Laboratory MARS, LR17ES05, ISITCom, University of Sousse, Sousse, Tunisia

    Mohamed Wajdi Ouertani, Ghaith Manita & Ouajdi Korbaa

  2. ENSI, University of Manouba, Manouba, Tunisia

    Mohamed Wajdi Ouertani

  3. ESEN, University of Manouba, Manouba, Tunisia

    Ghaith Manita

  4. ISITCom, University of Sousse, Sousse, Tunisia

    Ouajdi Korbaa

Authors
  1. Mohamed Wajdi Ouertani
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Ghaith Manita
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Ouajdi Korbaa
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Mohamed Wajdi Ouertani.

Ethics declarations

Conflict of interest

The authors declare that they have no conflict of interest.

Ethical approval

This article does not contain any studies with human participants or animals performed by any of the authors.

Additional information

Communicated by V. Loia.

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Ouertani, M.W., Manita, G. & Korbaa, O. Chaotic lightning search algorithm. Soft Comput 25, 2039–2055 (2021). https://doi.org/10.1007/s00500-020-05273-0

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00500-020-05273-0

Keywords

Search

Navigation