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The role of basic, modified and hybrid shuffled frog leaping algorithm on optimization problems: a review

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Abstract

Shuffled frog leaping algorithm (SFLA) is a meta-heuristic to handle different large-scale optimization problems. SFLA is a population-based algorithm that combines the advantages of memetic algorithm and particle swarm optimization. This paper compares previous researches on SFLA and its effectiveness, with the most applied optimization algorithms reviewed and analyzed. Based on the literature, many efforts by previous researchers on SFLA denote the next generations of basic SFLA with diverse structures for modified SFLA or hybrid SFLA. As well, an attempt is made to highlight these structures, their enhancements and advantages. Moreover, this paper considers top improvements on SFLA for solving multi-objective optimization problems, enhancing local and global exploration, avoiding being trapped into local optima, declining computational time and improving the quality of the initial population. The measured enhancements in SFLA are based on the statistical results obtained from 89 published papers and by considering the most common and effective modifications done by a large number of researchers. Finally, the quantitative validations address the SFLA as a robust algorithm employed in various applications which outperforms the other optimization algorithms.

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References

  • Alghazi A, Selim S, Elazouni A (2012) Performance of shuffled frog-leaping algorithm in finance-based scheduling. J Comput Civ Eng (JUNE) 26(3):396–408

  • Amiri B, Fathian M, Maroosi A (2009) Application of shuffled frog-leaping algorithm on clustering. Int J Adv Manuf Technol 45(1–2):199–209

    Article  Google Scholar 

  • Anita J, Raglend IJ (2012) Solution of unit commitment problem using shuffled frog leaping algorithm. In: Proceedings of the international conference on computing, electronics and electrical technologies, pp 109–115

  • Baghmisheh MT, Madani K, Navarbaf A (2011) A discrete shuffled frog optimization algorithm. Artif Intell Rev 36(4):267–284

    Article  Google Scholar 

  • Banati H, Mehta S (2012) SEVO?: bio-inspired analytical tool for uni-modal and multimodal optimization. In: Proceedings of the international conference, pp 557–566

  • Bhaduri A, Bhaduri A (2009) Color image segmentation using clonal selection-based shuffled frog leaping algorithm. In: Proceedings of the international conference on advances in recent technologies in communication and computing, pp 517–520

  • Chen G (2009) Combined economic emission dispatch using SFLA. In: Proceedings of the international conference on information engineering and computer science, pp 1–4

  • Chen G, Chen J, Duan X (2009) Power flow and dynamic optimal power flow including wind farms. In: Proceedings of the international conference on sustainable power generation and supply, pp 1–6

  • Chen MR, Li X et al (2011a) An improved shuffled frog-leaping algorithm for job-shop scheduling problem. In: Proceedings of the second international conference on innovations in bio-inspired computing and applications, pp 203–206

  • Chen T, Luo J, Hu Y (2011b) Component placement process optimization for multi-head surface mounting machine based on tabu search and improved shuffled frog-leaping algorithm. In: Proceedings of the 3rd international workshop on intelligent systems and applications, pp 1–4

  • Chittineni S, Pradeep A, Dinesh G (2011a) A parallel hybridization of clonal selection with shuffled frog leaping algorithm for solving global optimization problems (P-AISFLA). In: Proceedings of the second international conference on swarm, evolutionary and memetic computing, vol 2, pp 211–222

  • Chittineni S, Godavarthi D, Pradeep ANS (2011b) A modified and efficient shuffled frog leaping algorithm (MSFLA) for unsupervised data clustering. In: Proceedings of the advances in computing and communications, pp 543–551

  • Dorigo M (1992) Optimization, Learning and Natural Algorithms (in Italian). Ph.D. thesis, Dipartimento di Elettronica, Politecnico di Milano, Italy

  • Duan JH, Pan Q (2010) Scheduling the lot-streaming flow shop problem using a shuffled frog-leaping algorithm. In: Proceedings of the sixth international conference on natural computation (Icnc), pp 4263–4266

  • Eberhart RC, Kennedy J (1995) A new optimizer using particle swarm theory. In: Proceedings of the sixth international symposium on micro machine and human science, vol 1, IEEE Press, Nagoya, pp 39–43

  • Eghbal M, Saha T, Hasan K (2011) Transmission expansion planning by meta-heuristic techniques: a comparison of shuffled frog leaping algorithm, PSO and GA. Power and Energy Society General Meeting, IEEE, pp 1–8

  • Elbeltagi E, Hegazy T, Grierson D (2007) A modified shuffled frog-leaping optimization algorithm: applications to project management. Struct Infrastruct Eng 3(1):53–60

    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 

  • Eusuff MM (2004) Water resources decision making using meta-heuristic optimization methods. PhD. thesis, University of Arizona

  • Eusuff MM, Lansey K, Pasha F (2006) Shuffled frog-leaping algorithm: a memetic meta-heuristic for discrete optimization. Eng Optim 38(2):129–154

    Article  MathSciNet  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  MATH  MathSciNet  Google Scholar 

  • Gitizadeh M, Vahed AA, Aghaei J (2012) Multistage distribution system expansion planning considering distributed generation using hybrid evolutionary algorithms. Appl Energy 101:655–666

  • Gomez-Gonzalez M (2012) Shuffled frog-leaping algorithm for parameter estimation of a double-cage asynchronous machine. Power Appl IET 6(8):484–490

  • Gomez-Gonzalez M, Ruiz-Rodriguez FJ, Jurado F (2013) A binary SFLA for probabilistic three-phase load flow in unbalanced distribution systems with technical constraints. Int J Electr Power Energy Syst 48:48–57

    Article  Google Scholar 

  • Holland J (1973) Genetic algorithms and the optimal allocation of trials. SIAM J Comput 2(2):88–105

  • Huynh T (2008) A modified shuffled frog leaping algorithm for optimal tuning of multivariable PID controllers. Second international conference on genetic and evolutionary computing, pp 3–8

  • Huynh TH, Nguyen DH (2009) Fuzzy controller design using a new shuffled frog leaping algorithm. In: Proceedings of the IEEE international conference on industrial technology, pp 1–6

  • Jafari A, Bijami E, Bana HR, Sadri S (2012) A design automation system for CMOS analog integrated circuits using new hybrid shuffled frog leaping algorithm. Microelectron J 43(11):908–915

    Article  Google Scholar 

  • Jahani R et al (2010) Optimal placement of unified power flow controller in power system by a new advanced heuristic method. Int J Tech Phys Probl Eng 2(4):13–18

    Google Scholar 

  • Jahani R et al (2011) Optimal parameters of power system stabilizer for minimizing the maximum overshoot using SFLA. Am J Sci Res 32(32):58–68

    Google Scholar 

  • Karaboga D (2005) An idea based on honey bee swarm for numerical optimization. Technical report TR06, Erciyes University, Engineering Faculty, Computer Engineering Department

  • Kavousifard A, Samet H (2011) A novel method based on modified shuffled frog leaping algorithm and artificial neural network for power system load prediction. In: Emerging intelligent technologies in industry. Springer, Heidelberg, pp 35–46

  • Khorsandi A, Alimardani A, Vahidi B, Hosseinian SH (2011) Hybrid shuffled frog leaping algorithm and Nelder–Mead simplex search for optimal reactive power dispatch. IET Gener Transm Distrib 5(2):249–256

  • Kimiyaghalam A (2012) Application of IBSFLA and BSFLA approaches for locating of fault indicators in distribution networks. Distrib Netw 17:1–7

  • Kirkpatrick S, Gelatt CD, Vecchi MP (1983) Optimization by simulated annealing. Science 220(4598):671–680

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

    Article  MATH  MathSciNet  Google Scholar 

  • Li Q (2009) Shuffled frog leaping algorithm based optimal reactive power flow. In: Proceedings of the international symposium on computer network and multimedia technology, pp 1–4

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

    Article  Google Scholar 

  • Li Y, Xiu-fen K, Rui-qing H (2011a) Modified shuffled frog leaping algorithm applying on logistics distribution vehicle rounting problem. In: Proceedings of the 4th international conference on biomedical engineering and informatics (BMEI), pp 2277–2280

  • Li ZY, Yu CX, Zhang ZJ (2011b) Optimal algorithm of shuffled frog leaping based on immune evolutionary particle swarm optimization. Adv Mater Res 268–270:1188–1193

    Article  Google Scholar 

  • Li Y, Zhou J, Yang L, Qin H, Yang L (2008) The Chaos-based shuffled frog leaping algorithm and its application. In: Proceedings of the fourth international conference on natural computation, pp 481– 485

  • Li Z, Wang Y (2011a) An improved shuffled frog leaping algorithm for TSP. Adv Multimed Softw Eng Comput 2:139–144

    Google Scholar 

  • Li Y, Zhou J, Zhang Y, Qin H, Liu L (2010) Novel multiobjective shuffled frog leaping algorithm with application to reservoir flood control operation. J Water Resour Plan Manag 136(2):217–226

  • Lin J, Zhong Y, Zhang J (2012) A modified discrete shuffled flog leaping algorithm for RNA secondary structure prediction. In: Advances in control and communication. Springer, Heidelberg, pp 591–599

  • Lin J, Zhong Y, Zhang J (2011) Discrete shuffled flog leaping algorithm for RNA secondary structure prediction. In: Proceedings of the seventh international conference on natural computation, pp 1489– 1493

  • Lin MD et al (2007) Scatter search heuristic for least-cost design of water distribution networks. Eng Optim 39(7):857–876

    Article  Google Scholar 

  • Liping Z, Weiwei W, Han Y, Yefeng X, Yixian C (2012) Application of shuffled frog leaping algorithm to an uncapacitated SLLS problem. AASRI Conf Comput Intell Bioinform 1:226–231

    Google Scholar 

  • Liu J, Li Z, Hu X, Chen Y, Liu F (2012) Multi-objective dynamic population shuffled frog-leaping biclustering of microarray data. BMC Genomics 13(3):S6

    Article  Google Scholar 

  • Liu J, Li Z, Hu X, Chen Y (2011) Multi-objective optizition shuffled frog-leaping biclustering. In: Proceedings of the IEEE international conference on bioinformatics and biomedicine workshops (BIBMW), pp 151–156

  • Luo KP (2012) A shuffled frog leaping algorithm for solving vehicle routing problem. Appl Mech Mater 197:529–533

    Article  Google Scholar 

  • Luo XH, Yang Y, Li X (2008) Solving TSP with shuffled frog-leaping algorithm. In: Proceedings of the eighth international conference on intelligent systems design and applications, pp 228–232

  • Malekpour AR, Niknam T (2011) A probabilistic multi-objective daily Volt/Var control at distribution networks including renewable energy sources. Energy 36(5):3477–3488

    Article  Google Scholar 

  • Malekpour AR, Tabatabaei S, Niknam T (2012) Probabilistic approach to multi-objective Volt/Var control of distribution system considering hybrid fuel cell and wind energy sources using improved shuffled frog leaping algorithm. Renew Energy 39(1):228–240

    Article  Google Scholar 

  • Nejad HC, Jahani R, Sarlak GR (2011) Applying shuffled frog leaping algorithm for economic load dispatch of power system. Am J Sci Res 20(20):82–89

    Google Scholar 

  • Niknam T, Farsani A (2010) A hybrid self-adaptive particle swarm optimization and modified shuffled frog leaping algorithm for distribution feeder reconfiguration. Eng Appl Artif Intell 23(8):1340–1349

    Article  Google Scholar 

  • Niknam T, Nayeripour M (2011) An efficient multi-objective modified shuffled frog leaping algorithm for distribution feeder reconfiguration problem. Eur Trans Electr Power 21:721–739

    Article  Google Scholar 

  • Niknam T, Narimani MR, Jabbari M, Malekpour AR (2011a) A modified shuffle frog leaping algorithm for multi-objective optimal power flow. Energy 36(11):6420–6432

    Article  Google Scholar 

  • Niknam T, Zare M, Aghaei J, Farsani EA (2011b) A new hybrid evolutionary optimization algorithm for distribution feeder reconfiguration. Appl Artif Intell 25(10):951–971

    Article  Google Scholar 

  • Niknam T, Bahmani B, Doagou H (2011c) A new evolutionary algorithm for non-linear economic dispatch. Expert Syst Appl 38(10): 13301–13309

  • Niknam T, Farsani E, Nayeripour M, Firouzi B (2012) A new tribe modified shuffled frog leaping algorithm for multi-objective distribution feeder reconfiguration considering distributed generator units. Eur Trans Electr Power 22:308–333

    Article  Google Scholar 

  • Pakravesh H, Shojaei A (2011) Optimization of industrial CSTR for vinyl acetate polymerization using novel shuffled frog leaping based hybrid algorithms and dynamic modeling. Comput Chem Eng 35(11):2351–2365

    Article  Google Scholar 

  • Pan QK, Wang L, Gao L, Li J (2010) An effective shuffled frog-leaping algorithm for lot-streaming flow shop scheduling problem. Int J Adv Manuf Technol 52(5–8):699–713

    Google Scholar 

  • Pasha MFK, Lansey K (2009) Water quality parameter estimation for water distribution systems. Civ Eng Environ Syst 26(3):231–248

    Article  Google Scholar 

  • Perez I, Gomez-Gonzalez M, Jurado F (2012) Estimation of induction motor parameters using shuffled frog-leaping algorithm. Electr Eng 95(3):1–9

  • Pu H, Zhen Z, Wang D (2011) Modified shuffled frog leaping algorithm for optimization of UAV flight controller. Int J Intell Comput Cybern 4(1):25–39

    Article  MathSciNet  Google Scholar 

  • Rahimi-Vahed A, Mirzaei AH (2008a) A hybrid multi-objective shuffled frog-leaping algorithm for a mixed-model assembly line sequencing problem. Comput Ind Eng 53(4):642–666

    Article  Google Scholar 

  • Rahimi-Vahed A et al (2008b) A novel hybrid multi-objective shuffled frog-leaping algorithm for a bi-criteria permutation flow shop scheduling problem. Int J Adv Manuf Technol 41(11–12):1227–1239

    Google Scholar 

  • Rahimi-Vahed A, Mirzaei AH (2007) Solving a bi-criteria permutation flow-shop problem using shuffled frog-leaping algorithm. Soft Comput 12(5):435–452

    Article  Google Scholar 

  • Rameshkhah F (2011) Comparison of shuffled frog leaping algorithm and PSO in data clustering with constraint for grouping voltage control areas in power systems. Eur Trans Electr Power 21:1763– 1782

  • Rameshkhah F, Abedi M, Hosseinian SH (2010) Clustering of voltage control areas in power system using shuffled frog-leaping algorithm. Electr Eng 92(7–8):269–282

    Article  Google Scholar 

  • Rao RV (2011) Advanced modeling and optimization of manufacturing processes. Springer, London

    Book  Google Scholar 

  • Rao RV, Savsani VJ (2012) Mechanical design optimization using advanced optimization techniques. Springer, London, pp 5–34

  • Richard D (1976) The selfish gene. Oxford University Press, Oxford

  • Roy P, Chakrabarti A (2011) Modified shuffled frog leaping algorithm for solving economic load dispatch problem. Energy Power Eng 03(04):551–556

    Article  Google Scholar 

  • Roy P (2011) A new technique to solve minimum spanning tree (MST) problem using modified. In: Proceedings of the conference on advances in recent technologies in communication and computing, pp 32–35

  • Sardou IG, Banejad M, Hooshmand R, Dastfan A (2012) Modified shuffled frog leaping algorithm for optimal switch placement in distribution automation system using a multi-objective fuzzy approach. IET Gener Transm Distrib IET 6(6):493–502

    Article  Google Scholar 

  • Sayedi E, Farsangi M, Barati M, Lee Y (2012) A modified shuffled frog leaping algorithm for nonconvex economic dispatch problem. In: Power and energy society gene, pp 1–8

  • Seyedi E et al (2011) SVC multi-objective Var planning using SFL. Int J Tech Phys Probl Eng 7(3):76–80

    Google Scholar 

  • Shayanfar H, Jahani R, Olamaei J (2010a) Comparison of modified shuffled frog leaping algorithm and other heuristic methods for optimal placement of unified power flow controllers in electrical power systems. Aust J Basic Appl Sci 4(11):5590–5598

    Google Scholar 

  • Shayanfar H, Jahani R, Olamaei J (2010b) Modified shuffled frog leaping algorithm and other heuristic methods for optimal placement of unified power flow controllers in electrical power systems. Aust J Basic Appl Sci 4(11):5590–5598

  • Shirvani M, Shakeri P, Behzadipour E, Baghbani I (2012a) PID power system stabilizer design based on shuffled frog leaping algorithm. Life Sci J 9(2):1065–1070

    Google Scholar 

  • Shirvani M, Shakeri P, Behzadipour E, Baghbani I (2012b) Unified power flow controller design based on shuffled frog leaping algorithm. Life Sci J 9(2):1071–1076

    Google Scholar 

  • Srinivasa Reddy A, Vaisakh K (2013) Shuffled differential evolution for large scale economic dispatch. Electr Power Syst Res 96:237–245

    Article  Google Scholar 

  • Tavakolan M (2011) Applying the shuffled frog-leaping algorithm to improve scheduling of construction projects with activity splitting allowed. In: Proceedings of the management and innovation for a sustainable built environment, pp 1–9

  • Tavakolan M, Ashuri B (2012) Comparison of evolutionary algorithms in non-dominated solutions of time-cost-resource optimization problem. ASC Annu Int Conf Proc 48:1–9

    Google Scholar 

  • Teekeng W, Thammano A (2011) A combination of shuffled frog leaping and fuzzy logic for flexible job-shop scheduling problems. Procedia Comput Sci 6:69–75

    Article  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  MATH  MathSciNet  Google Scholar 

  • Wang M, Di W (2010) A modified shuffled frog leaping algorithm for the traveling salesman problem. In: Proceedings of the sixth international conference on natural computation (Icnc), pp 3701–3705

  • Wang N, Li X, Chen XH (2010) Fast three-dimensional Otsu thresholding with shuffled frog-leaping algorithm. Pattern Recognit Lett 31(13):1809–1815

  • Wang YM, Bao Y, Chen J, Li J (2011) A hybrid shuffled frog leaping algorithm for solving no\_idle permutation flow shop scheduling problems. Adv Eng Forum 1:110–115

    Article  Google Scholar 

  • Xu L, Fei M, Jia T, Yang TC (2012) Bandwidth scheduling and optimization using non-cooperative game model-based shuffled frog leaping algorithm in a networked learning control system. Neural Comput Appl 21(6):1117–1128

  • Xu Y, Wang L, Zhou G, Wang S (2011) An effective shuffled frog leaping algorithm for solving hybrid flow-shop scheduling problem. Adv Intell Comput 6838:560–567

  • Yammani C, Maheswarapu S, Matam S (2012) Multiobjective optimization for optimal placement and size of dg using shuffled frog leaping algorithm. Energy Procedia 14:990–995

    Article  Google Scholar 

  • Zhang X et al (2012) Power control algorithm in cognitive radio system based on modified shuffled frog leaping algorithm. AEU Int J Electron Commun 66(6):448–454

  • Zhang Y, Wang B (2010) Optimal viewpoint selection for volume rendering based on shuffled frog leaping algorithm. In: Proceedings of the IEEE international conference on progress in informatics and computing, pp 706–709

  • Zhen Z et al (2007) A novel memetic algorithm for global optimization based on PSO and SFLA. Adv Comput Intell 4683:127–136

  • Zhen Z, Wang D, Liu Y (2009) Improved shuffled frog leaping algorithm for continuous optimization problem. In: Proceedings of the IEEE congress on evolutionary computation, pp 2992–2995

  • Zhijin Z, Keqiang Y, Zhidong Z (2008) Discrete shuffled frog leaping algorithm for multi-user detection in DS-CDMA communication system. In: Proceedings of the 11th IEEE international conference on communication technology, pp 421–424

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Acknowledgments

The authors greatly acknowledge the Research Management Centre, UTM and Ministry of Higher Education Malaysia (MOHE) for financial support through the Fundamental Research Grant Scheme (FRGS) No. R.J130000.7828.4F170.

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Authors and Affiliations

  1. Soft Computing Research Group, Faculty of Computing, Universiti Teknologi Malaysia, 81310 , UTM Skudai, Johor, Malaysia

    Arezoo Sarkheyli & Azlan Mohd Zain

  2. Department of Manufacturing and Industrial Engineering, Faculty of Mechanical Engineering, Universiti Teknologi Malaysia, 81310 , UTM Skudai, Johor, Malaysia

    Safian Sharif

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  1. Arezoo Sarkheyli
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  2. Azlan Mohd Zain
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Correspondence to Arezoo Sarkheyli.

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Communicated by V. Loia.

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Sarkheyli, A., Zain, A.M. & Sharif, S. The role of basic, modified and hybrid shuffled frog leaping algorithm on optimization problems: a review. Soft Comput 19, 2011–2038 (2015). https://doi.org/10.1007/s00500-014-1388-4

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