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Parameter tuning in metaheuristics: a bibliometric and gap analysis

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Abstract

Parameters are an integral part of every metaheuristic algorithm as these control different aspects of algorithms. Thus, parameter tuning (PT) approaches are one of the prime sub-domains of metaheuristics. The proposed study presents two analyses of PT approaches (i) Bibliometric analysis (BA), for a quantitative overview of the literature giving a multi-dimensional view of studies carried down, and (ii) gap analysis (GA), for identifying voids in the research field. This analysis has been conducted for the time period 2002–2022 over the Scopus database. This study enables a macroscopic view of the field of PT and enables researchers to determine the gaps existing in PT methods, paving the way for future research. To the best of our knowledge, this is the first study presenting a BA and GA over PT methods.

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Abbreviations

BA:

Bibliometric analysis

GA:

Gap analysis

PT:

Parameter tuning

DOE:

Design of experiment

DACE:

Design and analysis of computer experiments

EA:

Evolutionary algorithm

ILS:

Iterated local search

RSM:

Response surface model

References

  1. Revanna JKC, Al-Nakash NYB (2023) Metaheuristic link prediction (mlp) using ai based aco-ga optimization model for solving vehicle routing problem. Int J Inf Technol 15:3425–3439

    Google Scholar 

  2. Ajmera K, Tewari TK (2023) Energy-efficient virtual machine scheduling in iaas cloud environment using energy-aware green-particle swarm optimization. Int J Inf Technol 15:1927–1935

    Google Scholar 

  3. Wasson V, Kaur B (2023) Grey wolf optimizer based iqa of mixed and multiple distorted images, International Journal of Information Technology 1–11

  4. Prasad SBR, Chandana BS (2023) Mobilenetv3: a deep learning technique for human face expressions identification. Int J Inf Technol 15:3229–3243

    Google Scholar 

  5. Goyal S, Bhatia PK (2021) Software fault prediction using lion optimization algorithm. Int J Inf Technol 13:2185–2190

    Google Scholar 

  6. Aleti A, Moser I (2016) A systematic literature review of adaptive parameter control methods for evolutionary algorithms. ACM Computing Surveys (CSUR) 49:1–35

    Article  Google Scholar 

  7. de Lacerda MGP, de Araujo Pessoa LF, de Lima Neto FB, Ludermir TB, Kuchen H (2021) A systematic literature review on general parameter control for evolutionary and swarm-based algorithms, Swarm and Evolutionary Computation 60 100777

  8. Huang C, Li Y, Yao X (2019) A survey of automatic parameter tuning methods for metaheuristics. IEEE Trans Evol Comput 24:201–216

    Article  Google Scholar 

  9. Eiben AE, Smit SK (2011) Parameter tuning for configuring and analyzing evolutionary algorithms. Swarm Evol Comput 1:19–31

    Article  Google Scholar 

  10. Joshi SK, Bansal JC (2020) Parameter tuning for meta-heuristics. Knowl-Based Syst 189:105094

    Article  Google Scholar 

  11. Burnham JF (2006) Scopus database: a review. Biomedical digital libraries 3:1–8

    Article  PubMed  PubMed Central  Google Scholar 

  12. Yuan B, Gallagher M (2004) Statistical racing techniques for improved empirical evaluation of evolutionary algorithms, in: International Conference on Parallel Problem Solving from Nature, Springer, pp. 172–181

  13. Birattari M, Stützle T, Paquete L, Varrentrapp K et al. (2002) A racing algorithm for configuring metaheuristics., in: Gecco, volume 2, Citeseer, pp. 1152–1157

  14. Adenso-Diaz B, Laguna M (2006) Fine-tuning of algorithms using fractional experimental designs and local search. Oper Res 54:99–114

    Article  Google Scholar 

  15. Nannen V, Eiben AE, Efficient relevance estimation and value calibration of evolutionary algorithm parameters, in: 2007 IEEE congress on evolutionary computation. IEEE 2007:103–110

  16. Hutter F, Hoos HH, Stützle T (2007) Automatic algorithm configuration based on local search, in: Aaai, volume 7, pp. 1152–1157

  17. Bartz-Beielstein T, Lasarczyk CW, Preuß M, Sequential parameter optimization, in, (2005) IEEE congress on evolutionary computation, volume 1. IEEE 2005:773–780

  18. Balaprakash P, Birattari M, Stützle T (2007) Improvement strategies for the f-race algorithm: Sampling design and iterative refinement, in: Hybrid Metaheuristics: 4th International Workshop, HM 2007, Dortmund, Germany, October 8-9. Proceedings 4, Springer, 2007, pp. 108–122

  19. Veček N, Mernik M, Črepinšek 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 

  20. Hutter F, Hoos HH, Leyton-Brown K (2011) Sequential model-based optimization for general algorithm configuration, in: Learning and Intelligent Optimization: 5th International Conference, LION 5, Rome, Italy, January 17-21, 2011. Selected Papers 5, Springer, pp. 507–523

  21. Hansen N (2006) The cma evolution strategy: a comparing review. Advances in the estimation of distribution algorithms, Towards a new evolutionary computation, pp 75–102

    Google Scholar 

  22. Barbosa EB, Senne ELF (2017) A heuristic for optimization of metaheuristics by means of statistical methods., in: ICORES, pp. 203–210

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Authors and Affiliations

  1. Department of Computer Science, Aligarh Muslim University, Aligarh, Uttar Pradesh, 202002, India

    Deepika Kaushik & Mohammad Nadeem

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  1. Deepika Kaushik
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  2. Mohammad Nadeem
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Correspondence to Deepika Kaushik.

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Kaushik, D., Nadeem, M. Parameter tuning in metaheuristics: a bibliometric and gap analysis. Int. j. inf. tecnol. 16, 1645–1651 (2024). https://doi.org/10.1007/s41870-023-01694-w

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