Abstract
Shuffled Frog leaping algorithm (SFLA) is a multi population swarm intelligence algorithm which employs population partitioning techniques during the evolutionary stage. Methods adopted by SFLA for partitioning the population into memeplexes play a critical role in determining its ability to solve complex optimization problems. However, limited research is done in this direction. This work presents supervised machine learning based methods Spectral Partitioning (SCP), Agglomerative Partitioning (AGP) and Ward Hierarchical Partitioning (WHP) for distributing the solutions into memeplexes. The efficacy of variants of SFLA with these methods is assessed over CEC2015 Bound Constrained Single-Objective Computationally Expensive Numerical Optimisation problems. Analysis of results establishes that proposed SCP, AGP and WHP methods outperform Shuffled complex evolution (SCE) partitioning technique; Seed and distance based partitioning technique (SEED), Random partitioning (RAND) and Dynamic sub-swarm partitioning (DNS) for more than 10 functions. Time complexity of all the algorithms is comparable with each other. Statistical analysis using Wilcoxon signed rank sum test indicates that SCP, AGP and WHP perform significantly better than existing approaches for small dimensions.
This is a preview of subscription content, log in via an institution to check access.
Similar content being viewed by others
Data availability
Does not contain explicit data code availability on request Authors’ contributions All work is done by the author only.
References
Azizipanah-Abarghooee R, Narimani MR, Bahmani-Firouzi B, Niknam T (2014) Modified shuffled frog leaping algorithm for multi-objective optimal power flow with facts devices. J Intell Fuzzy Syst 26:681–692. https://doi.org/10.3233/IFS-120759
Banati H, Mehta S (2013) Improved shuffled frog leaping algorithm for continuous optimisation adapted sevo toolbox. Int J Adv Intell Paradigms 5:31–44. https://doi.org/10.1504/IJAIP.2013.054670
Cai J, Zhou R, Lei D (2020) Dynamic shuffled frog-leaping algorithm for distributed hybrid flow shop scheduling with multiprocessor tasks. Eng Appl Artif Intell 90:103540. https://doi.org/10.1016/j.engappai.2020.103540
Cai J, Lei D, Li M (2020) A shuffled frog-leaping algorithm with memeplex quality for bi-objective distributed scheduling in hybrid flow shop. Int J Prod Res 59:1–18. https://doi.org/10.1080/00207543.2020.1780333
Chaudhary R, Banati H (2020) Study of population partitioning techniques on efficiency of swarm algorithms. Swarm Evol Comput 55:100672. https://doi.org/10.1016/j.swevo.2020.100672
Chen Q, Liu B, Zhang QF, Liang JJ, Suganthan PN, Qu B (2015) Problem definitions and evaluation criteria for cec 2015 special session on bound constrained single-objective computationally expensive numerical optimization
Daoden K, Thaiupthump T (2016) A modified shuffled frog leaping algorithm using truncated gaussian distribution in Frog’s position updating process, pp 965–974. https://doi.org/10.1007/978-981-10-0557-2_92
Del Ser J, Osaba E, Molina D, Yang X-S, Salcedo-Sanz S, Camacho D, Das S, Suganthan PN, Coello Coello CA, Herrera F (2019) Bio-inspired computation: where we stand and what’s next. Swarm Evol Comput 48:220–250. https://doi.org/10.1016/j.swevo.2019.04.008
Ding W, Wang J (2013) A novel approach to minimum attribute reduction based on quantum-inspired self-adaptive cooperative co-evolution. Knowl Based Syst 50:1–13. https://doi.org/10.1016/j.knosys.2013.03.008
Duan Q, Gupta V, Sorooshian S (1993) A shuffled complex evolution approach for effective and efficient global minimization. J Optim Theory Appl 76:501–521. https://doi.org/10.1007/BF00939380
Duan X, Niu T, Huang Q (2018) An improved shuffled frog leaping algorithm and its application in dynamic emergency vehicle dispatching. Math Probl Eng 2018:1–34. https://doi.org/10.1155/2018/7896926
Duarte G, Lemonge A, Goliatt L (2017) A dynamic migration policy to the island model, pp 1135–1142. https://doi.org/10.1109/CEC.2017.7969434
Eusuff M, Lansey K, Pasha F (2006) Shuffled frog-leaping algorithm: a memetic meta-heuristic for discrete optimization. Eng Optim 38(2):129–154. https://doi.org/10.1080/03052150500384759
Guo Y, Tian X, Fang G, Xu Y-P (2020) Many-objective optimization with improved shuffled frog leaping algorithm for inter-basin water transfers Adv. Water Resour 138:103531. https://doi.org/10.1016/j.advwatres.2020.103531
Jaballah S, Rouis K, Ben Abdallah F, Tahar J (2014) An improved shuffled frog leaping algorithm with a fast search strategy for optimization problems, pp 23–27. https://doi.org/10.1109/ICCP.2014.6936975
Jaddi N, Abdullah S, Hamdan A (2015) Multi-population cooperative bat algorithm-based optimization of artificial neural network model. Inf Sci 294:628–644. https://doi.org/10.1016/j.ins.2014.08.050
Jadidoleslam M, Ebrahimi A (2015) Reliability constrained generation expansion planning by a modified shuffled frog leaping algorithm. Int J Electr Power Energy Syst 64:743–751. https://doi.org/10.1016/j.ijepes.2014.07.073
Jaggi P, Mehta S (2016) Resource provisioning and work flow scheduling in clouds using augmented shuffled frog leaping algorithm. J Parallel Distrib Comput 101:3. https://doi.org/10.1016/j.jpdc.2016.11.003
Karpagam M, Geetha K, Chinnasamy R (2020) A modified shuffled frog leaping algorithm for scientific workflow scheduling using clustering techniques. Soft Comput 24:4. https://doi.org/10.1007/s00500-019-04484-4
Kashtiban AM, Ahandani MA (2009) Various strategies for partitioning of memeplexes in shuffled frog leaping algorithm. In: 2009 14th International CSI Computer Conference, pp 576–581. https://doi.org/10.1109/CSICC.2009.5349641
Kordestani JK, Ranginkaman AE, Meybodi MR, Novoa-Hernández P (2019) A novel framework for improving multi-population algorithms for dynamic optimization problems, (2019) A scheduling approach. Swarm Evol Comput 44:788–805. https://doi.org/10.1016/j.swevo.2018.09.002
Laessig J, Sudholt D (2010) The benefit of migration in parallel evolutionary algorithms, pp 1105–1112. https://doi.org/10.1145/1830483.1830687
Lei D, Wang T (2019) Solving distributed two-stage hybrid flowshop scheduling using a shuffled frog-leaping algorithm with memeplex grouping. Eng Optim 52:1–14. https://doi.org/10.1080/0305215X.2019.1674295
Li X (2004) Adaptively choosing neighbourhood bests using species in a particle swarm optimizer for multimodal function optimization, vol. 3102. https://doi.org/10.1007/978-3-540-24854-5_10
Li C, Yang S (2012) A general framework of multipopulation methods with clustering in undetectable dynamic environments. IEEE Trans Evol Comput 16(4):556–577. https://doi.org/10.1109/TEVC.2011.2169966
Ma H, Shen S, Yu M, Yang Z, Fei M, Zhou H (2019) Multi-population techniques in nature inspired optimization algorithms: a comprehensive survey. Swarm Evol Comput 44:365–387. https://doi.org/10.1016/j.swevo.2018.04.011
Ma X, Bian Y, Gao F (2020) An improved shuffled frog leaping algorithm for multiload agv dispatching in automated container terminals. Math Probl Eng 2020:1–13. https://doi.org/10.1155/2020/1260196
Manning CD, Raghavan P, Schütze H (2008) Introduction to Information Retrieval. Cambridge University Press, Cambridge, UK. http://nlp.stanford.edu/IR-book/information-retrieval-book.html
Mehta S, Banati H (2014) Context aware filtering using social behavior of frogs. Swarm Evol Comput 17:25–36. https://doi.org/10.1016/j.swevo.2014.02.003
Parrott D, Li X (2006) Locating and tracking multiple dynamic optima by a particle swarm model using speciation. IEEE Trans Evol Comput 10:440–458. https://doi.org/10.1109/TEVC.2005.859468
Shi J, Malik J (2002) Normalized cuts and image segmentation. IEEE Transactions on Pattern Analysis and Machine Intelligence 22. https://doi.org/10.1109/34.868688
Skolicki Z, De Jong K (2005) The influence of migration intervals on island models, pp 1295–1302. https://doi.org/10.1145/1068009.1068219
Tang J, Zhang R, Wang P, Zhao Z, Fan L, Liu X (2020) A discrete shuffled frog-leaping algorithm to identify influential nodes for influence maximization in social networks. Knowl Based Syst 187:104833. https://doi.org/10.1016/j.knosys.2019.07.004
Wang L, Gong Y (2013) Quantum binary shuffled frog leaping algorithm, pp. 1655–1659. https://doi.org/10.1109/IMCCC.2013.366
Ward JH (1963) Hierarchical groupings to optimize an objective function. J Am Stat Assoc 234–244
Xia X, Gui L, Zhan ZH (2018) A multi-swarm particle swarm optimization algorithm based on dynamical topology and purposeful detecting. Appl Soft Comput 67:42. https://doi.org/10.1016/j.asoc.2018.02.042
Yang X-S (2013) Swarm intelligence based algorithms: a critical analysis. Evol Intell 7(1):17–28. https://doi.org/10.1007/s12065-013-0102-2
Funding
Not applicable.
Author information
Authors and Affiliations
Corresponding author
Ethics declarations
Conflict of interest
The author declares no conflict of interest.
Additional information
Publisher's Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Rights and permissions
Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.
About this article
Cite this article
Mehta, S. Improved shuffled Frog leaping algorithm with unsupervised population partitioning strategies for complex optimization problems. J Comb Optim 47, 6 (2024). https://doi.org/10.1007/s10878-023-01102-w
Accepted:
Published:
DOI: https://doi.org/10.1007/s10878-023-01102-w