Skip to main content
SpringerLink
Log in

Memetic frog leaping algorithm for global optimization

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

Abstract

Developing an effective memetic algorithm that integrates leaning units and achieves the synergistic coordination between exploration and exploitation is a difficult task. In this paper, we propose a memetic algorithm based on the shuffled frog leaping algorithm, which is fulfilled by three units: memetic diffusion component, memetic evolutionary component and memetic learning component. Memetic diffusion component enhances the diversity of population by the shuffled process. Memetic evolutionary component accomplishes the exploitation task by integrating the frog leaping rule, geometric center, Newton’s gravitational force-based gravitational center and Lévy flight operator. Memetic learning component improves the exploration by an adaptive learning rule based on the individual selection and the dimension selection. In order to evaluate the effectiveness of the proposed algorithm, 30 benchmark functions and a real-world optimization problem are used to compare our algorithm against 13 well-known heuristic methods. The experimental results demonstrate that the performance of our algorithm is better than others for the continuous optimization problems.

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

Similar content being viewed by others

References

  • Ahandani MA (2014) A diversified shuffled frog leaping: an application for parameter identification. Appl Math Comput 239:1–16

    MathSciNet  MATH  Google Scholar 

  • Ahandani MA, Alavi-Rad H (2015) Opposition-based learning in shuffled frog leaping: an application for parameter identification. Inf Sci 291:19–42

    Article  Google Scholar 

  • Bambha NK, Bhattacharyya SS, Teich J, Zitzler E (2004) Systematic integration of parameterized local search into evolutionary algorithms. IEEE Trans Evolut Comput 8(2):137–155

    Article  Google Scholar 

  • Bhattacharjee KK, Sarmah SP (2014) Shuffled frog leaping algorithm and its application to 0/1 knapsack problem. Appl Soft Comput 19:252–263

    Article  Google Scholar 

  • Bo J, Yuchun T, Yang-Qing Z, Chung-Dar L, Weber I (2005) Support vector machine with the fuzzy hybrid kernel for protein subcellular localization classification. In: Proceedings of IEEE international conference on fuzzy systems (FUZZ’05), Reno, NV, pp 420–423

  • Brest J, Greiner S, Boskovic B, Mernik M, Zumer V (2006) Self-adapting control parameters in differential evolution: a comparative study on numerical benchmark problems. IEEE Trans Evolut Comput 10(6):646–657

    Article  Google Scholar 

  • Cheng C, Zhang Y, Song M, Cheng G, Guo D, Cao J, Bao X (2014) Quantum-inspired shuffled frog leaping algorithm for spectrum sensing in cooperative cognitive radio network. In: International conference on human centered computing. Springer International Publishing, pp 80–92

  • Chow CK, Yuen SY (2011) An evolutionary algorithm that makes decision based on the entire previous search history. IEEE Trans Evolut Comput 15:741–768

    Article  Google Scholar 

  • Dawkins R (1976) The selfish gene. Clarendon Press, Oxford

    Google Scholar 

  • Derrac J, García S, Molina D, Herrera F (2011) A practical tutorial on the use of nonparametric statistical tests as a methodology for comparing evolutionary and swarm intelligence algorithms. Swarm Evolut Comput 1:3–18

    Article  Google Scholar 

  • Eusuff MM, Lansey KE (2003) Optimization of water distribution network design using the shuffled frog leaping algorithm. J Water Resour Plan Manag 129(3):210–225

    Article  Google Scholar 

  • Fang C, Wang L (2012) An effective shuffled frog-leaping algorithm for resource-constrained project scheduling problem. Comput Oper Res 39(5):890–901

    Article  MathSciNet  MATH  Google Scholar 

  • Holliday D, Resnick R, Walker J (1993) Fundamentals of physics. Wiley, New York

    Google Scholar 

  • Huang H, Qin H, Hao ZF, Lim A (2012) Example-based learning particle swarm optimization for continuous optimization. Inf Sci 182:125–138

    Article  MathSciNet  MATH  Google Scholar 

  • Iacca G, Neri F, Mininno E, Ong Y-S, Lim M-H (2012) Ockham’s Razor in memetic computing: three stage optimal memetic exploration. Inf Sci 188:17–43

    Article  MathSciNet  Google Scholar 

  • Karaboga D (2005) An idea based on honey bee swarm for numerical optimization, technical report TR06, Erciyes University

  • Kennedy J, Eberhart RC (1995) Particle swarm optimization. In: Proceeding IEEE international conference neural network, Perth, Western Australia, pp 1942–1948

  • Kóczy LT, Földesi P, Tü˝u-Szabó B (2017) Inf Sci 000: 1–12

  • Lam AYS, Li VOK, Yu JJQ (2012) Real-coded chemical reaction optimization. IEEE Trans Evol Comput 16:339–353

    Article  Google Scholar 

  • Le MN, Ong YS, Jin Y, Sendhoff B (2012) A unified framework for symbiosis of evolutionary mechanisms with application to water clusters potential model design. IEEE Comput Intell Mag 7(1):20–35

    Article  Google Scholar 

  • Lei D, Guo X (2015) A shuffled frog-leaping algorithm for hybrid flow shop scheduling with two agents. Expert Syst Appl 42(23):9333–9339

    Article  Google Scholar 

  • Li J, Pan Q, Xie S (2012a) An effective shuffled frog-leaping algorithm for multi-objective flexible job shop scheduling problems. Appl Math Comput 218(18):9353–9371

    MathSciNet  MATH  Google Scholar 

  • Li X, Luo J, Chen M-R, Wang N (2012b) An improved shuffled frog-leaping algorithm with extremal optimisation for continuous optimisation. Inf Sci 192:143–151

    Article  Google Scholar 

  • Li Y, Jiao L, Li P, Bo W (2014) A hybrid memetic algorithm for global optimization. Neurocomputing 134:132–139

    Article  Google Scholar 

  • Liang JJ, Qu BY, Suganthan PN (2013) Problem definitions and evaluation criteria for the CEC 2014 special session and competition on single objective real-parameter numerical optimization. Comput Intell Lab, Zhengzhou Univ., Zhengzhou, China, and Nanyang Technol. Univ., Singapore, Tech. Rep. 201311, Dec

  • Liao T, Aydın D, Stützle T (2013) Artificial bee colonies for continuous optimization: experimental analysis and improvements. Swarm Intel 7(4):327–356

    Article  Google Scholar 

  • Lim WH, Isa NAM (2014) Bidirectional teaching and peer-learning particle swarm optimization. Inf Sci 280:111–134

    Article  Google Scholar 

  • Liu H, Yi F, Yang H (2016) Adaptive grouping cloud model shuffled frog leaping algorithm for solving continuous optimization problems. Comput Intell Neuro 2016:25

    Google Scholar 

  • Liu C, Niu P, Li G et al (2018a) Enhanced shuffled frog-leaping algorithm for solving numerical function optimization problems. J Intell Manuf 29:1133–1153

    Article  Google Scholar 

  • Liu W, Gong M, Wang S, Ma L (2018b) A two-level learning strategy based memetic algorithm for enhancing community robustness of networks. Inf Sci 422:290–304

    Article  Google Scholar 

  • Luo X-H, Ye Y, Xia L (2008) Solving TSP with shuffled frog-leaping algorithm. In: Eighth international conference on intelligent system design and application, ISDA’08, 3

  • Luo J et al (2015) A novel hybrid shuffled frog leaping algorithm for vehicle routing problem with time windows. Inf Sci 316:266–292

    Article  Google Scholar 

  • Mantegna RN (1991) Levy walks and enhanced diffusion in Milan stock exchange. Phys A 179:232–242

    Article  Google Scholar 

  • Mantegna RN (1994) Fast, accurate algorithm for numerical simulation of Levy stable stochastic process. Phys Rev E 5(49):4677–4683

    Article  Google Scholar 

  • Merz CJ, Blake CL (2015) UCI repository of machine learning databases. http://www.ics.uci.edu/-mlearn/MLRepository.html

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

    Article  Google Scholar 

  • Moscato P (1989) On evolution, search, optimization, genetic algorithms and martial arts: towards memetic algorithms, technical reports. 826, Caltech concurrent computation program

  • Moscato P, Norman M (1989) A competitive and cooperative approach to complex combinatorial search, technical reports. 790, Caltech Concurrent Computation Program

  • Müller CL, Baumgartner B, Sbalzarini IF (2009) Particle swarm CMA evolution strategy for the optimization of multi-funnel landscapes. In: Proceedings of IEEE congress evolutionary computation, pp 18–21

  • Narimani MR (2011) A new modified shuffle frog leaping algorithm for non-smooth economic dispatch. World Appl Sci J 12(6):803–814

    Google Scholar 

  • Nguyen QH, Ong Y-S, Lim MH (2009) A probabilistic memetic framework. IEEE Trans Evolut Comput 13(3):604–623

    Article  Google Scholar 

  • Omran MGH, Engelbrecht AP, Salman A (2007) Differential evolution based particle swarm optimization. In: Proceedings of swarm intelligence symposium, Honolulu, HI, USA, pp 112–119

  • Ong YS, Keane AJ (2004) Meta-Lamarckian learning in memetic algorithm. IEEE Trans Evol Comput 8(2):99–110

    Article  Google Scholar 

  • Ong Y-S, Lim M-H, Zhu N, Wong K-W (2006a) Classification of adaptive memetic algorithms: a comparative study. IEEE Trans Syst Man Cybern B Cybern 36(1):141–152

    Article  Google Scholar 

  • Ong Y-S, Lim M-H, Zhu N, Wong K-W (2006b) Classification of adaptive memetic algorithms: a comparative study. IEEE Trans Syst Man Cybern B Cybern 36(1):141–152

    Article  Google Scholar 

  • Ong YS, Lim MH, Chen X (2010) Memetic computation—past, present & future [research frontier]. IEEE Comput Intell Mag 5(2):24–31

    Article  Google Scholar 

  • Ou Y, Sun Y (2011) Grid task scheduling strategy based on improved shuffled frog leaping algorithm. Comput Eng 37(21):146–151

    Google Scholar 

  • Qin AK, Huang VL, Suganthan PN (2009) Differential evolution algorithm with strategy adaptation for global numerical optimization. IEEE Trans Evolut Comput 13(2):398–417

    Article  Google Scholar 

  • Rao RV, Savsani VJ, Vakharia DP (2012) Teaching–learning-based optimization: an optimization method for continuous non-linear large scale problems. Inf Sci 183:1–15

    Article  MathSciNet  Google Scholar 

  • Rashedi E, Nezamabadi-Pour H, Saryazdi S (2009) GSA: a gravitational search algorithm. Inf Sci 179:2232–2248

    Article  MATH  Google Scholar 

  • Roy P, Roy P, Chakrabarti A (2013) Modified shuffled frog leaping algorithm with genetic algorithm crossover for solving economic load dispatch problem with valve-point effect. Appl Soft Comput 13(11):4244–4252

    Article  Google Scholar 

  • Samma H, Lim CP, Saleh JM (2016) A new reinforcement learning-based memetic particle swarm optimizer. Appl Soft Comput 43:276–297

    Article  Google Scholar 

  • Schutz B (2003) Gravity from the ground up. Cambridge University Press, Cambridge

    Book  Google Scholar 

  • Sharma S, Sharma TK, Pant M et al (2015) Centroid mutation embedded shuffled frog-leaping algorithm. Proc Comput Sci 46:127–134

    Article  Google Scholar 

  • Shin HJ, Eom DH, Kim SS (2005) One-class support vector machine-an application in machine fault detection and classification. Comput Ind Eng 48(2):395–408

    Article  Google Scholar 

  • Simon D (2008) Biogeography-based optimization. IEEE Trans Evolut Comput 12:702–713

    Article  Google Scholar 

  • Smith J (2007a) Coevolving memetic algorithms: a review and progress report. IEEE Trans Syst Man Cybern B Cybern 37(1):6–17

    Article  MathSciNet  Google Scholar 

  • Smith JE (2007b) Coevolving memetic algorithms: a review and progress report. IEEE Trans Syst Man Cybern B Cybern 37(1):6–17

    Article  MathSciNet  Google Scholar 

  • Storn RM, Price KV (1997) Differential evolution—a simple and efficient heuristic for global optimization over continuous spaces. J Glob Optim 11:341–359

    Article  MathSciNet  MATH  Google Scholar 

  • Sun J, Garibaldi JM, Krasnogor N, Zhang Q (2013) An intelligent multi-restart memetic algorithm for box constrained global optimization. Evolut Comput 21(1):107–147

    Article  Google Scholar 

  • Tang D, Cai Y, Zhao J, Xue Y (2014) A quantum behaved particle swarm optimization with memetic algorithm and memory for continuous non-linear large scale problems. Inf Sci 289:162–189

    Article  Google Scholar 

  • Tang D, Dong S, Jiang Y, Li H, Huang Y (2015) ITGO: invasive tumor growth optimization algorithm. Appl Soft Comput 36:670–698

    Article  Google Scholar 

  • Tang D, Yang J, Dong S, Liu Z (2016) A Lévy flight-based shuffled frog-leaping algorithm and its applications for continuous optimization problems. Appl Soft Comput 49:641–662

    Article  Google Scholar 

  • Uymaz SA, Tezel G, Yel E (2015) Artificial algae algorithm (AAA) for nonlinear global optimization. Appl Soft Comput 31:153–171

    Article  Google Scholar 

  • Vapnik V (1995) The nature of statistical learning theory. Springer, New York

    Book  MATH  Google Scholar 

  • Vasan P (2014) Handbook of research on artificial intelligence techniques and algorithms (2 volumes). https://doi.org/10.4018/978-1-4666-7258-1

  • Vasant P, Weber G-W, Dieu VN (2016) Handbook of research on modern optimization algorithms and applications in engineering and economics. https://doi.org/10.4018/978-1-4666-9644-0

  • Vecˇek N, Mernik M, Cˇ repinšek M (2014) A chess rating system for evolutionary algorithms: a new method for the comparison and ranking of evolutionary algorithms. Inf Sci 277:656–679

    Article  MathSciNet  Google Scholar 

  • Wang L, Fang C (2011) An effective shuffled frog-leaping algorithm for multi-mode resource-constrained project scheduling problem. Inf Sci 181(20):4804–4822

    Article  MathSciNet  MATH  Google Scholar 

  • Wang H, Moon I, Yang S, Wanga D (2012) A memetic particle swarm optimization algorithm for multimodal optimization problems. Inf Sci 197:38–52

    Article  Google Scholar 

  • Wang H, Zhang K, Tu X (2015) A mnemonic shuffled frog leaping algorithm with cooperation and mutation. Appl Intell 43(1):32–48

    Article  Google Scholar 

  • Wolpert DH, Macready WG (1997) No free lunch theorems for optimization. IEEE Trans Evolut Comput 1(1):67–82

    Article  Google Scholar 

  • Yang XS, Deb S (2009) Cuckoo search via L´evy flights. Proceedings of world congress on nature and biologically inspired comput IEEE Publications, USA, pp 210–214

  • Zhang J, Sanderson AC (2009) JADE: adaptive differential evolution with optional external archive. IEEE Trans Evolut Comput 13(5):945–958

    Article  Google Scholar 

  • Zhou HG, Yang CD (2006) Using immune algorithm to optimize anomaly detection based on SVM. In: Proceedings of IEEE international machine learning and cybernetics conference, Dalian, China, pp 4257–4261

  • Zhou Z, Ong Y-S, Nair P, Keane A, Lum K-Y (2007) Combining global and local surrogate models to accelerate evolutionary optimization. IEEE Trans Syst Man Cybern C Appl Rev 37(1):66–76

    Article  Google Scholar 

Download references

Acknowledgements

The authors would like to thank the reviewers and editor for their very useful and constructive comments that helped to improve the quality of the paper. This work is supported by the Guang Dong Provincial Natural fund project, Drug-target interaction prediction method based on collaborative intelligent optimization (2016A030310300); the Natural Science Foundation of China under Grant (No. 61501128); NSFC, Research on reasoning of behavior trust for resisting collusive reputation attack (71401045); Guangdong province precise medicine and big data engineering technology research center for traditional Chinese medicine, Guang Dong Provincial Natural fund (2014A030313585, 2015A030310267, 2015A030310483). Major scientific research projects of Guangdong, Research of Behavioral Trust resisting collusion reputation attack based on implicit and explicit big behavior data analysis (2017WTSCX021). Philosophy and Social Sciences of Guangzhou ‘13th Five-Year’ program (2018GZGJ48).

Author information

Authors and Affiliations

  1. School of Medical Information and Engineering, Guangdong Pharmaceutical University, Guangzhou, 510006, China

    Deyu Tang, Zhen Liu & Jin Yang

  2. School of Computer Science and Engineering, South China University of Technology, Guangzhou, 510006, China

    Deyu Tang & Jin Yang

  3. Department of Information Management Engineering, School of Management, Guangdong University of Technology, Guangzhou, 510520, China

    Jie Zhao

  4. Department of Computer Science, American University, Washington, DC, 20016, USA

    Zhen Liu

Authors
  1. Deyu Tang
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Zhen Liu
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Jin Yang
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Jie Zhao
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Deyu Tang.

Ethics declarations

Conflict of interest

The author declares that there is no conflict of interest.

Ethical approval

The work of this article does not involve use of human participants or animals.

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

Tang, D., Liu, Z., Yang, J. et al. Memetic frog leaping algorithm for global optimization. Soft Comput 23, 11077–11105 (2019). https://doi.org/10.1007/s00500-018-3662-3

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00500-018-3662-3

Keywords

Search

Navigation