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dc.title | Impact of boundary control methods on bound-constrained optimization benchmarking | en |
dc.contributor.author | Kadavý, Tomáš | |
dc.contributor.author | Viktorin, Adam | |
dc.contributor.author | Kazíková, Anežka | |
dc.contributor.author | Pluháček, Michal | |
dc.contributor.author | Šenkeřík, Roman | |
dc.relation.ispartof | IEEE Transactions on Evolutionary Computation | |
dc.identifier.issn | 1089-778X Scopus Sources, Sherpa/RoMEO, JCR | |
dc.identifier.issn | 1941-0026 Scopus Sources, Sherpa/RoMEO, JCR | |
dc.date.issued | 2022 | |
utb.relation.volume | 26 | |
utb.relation.issue | 6 | |
dc.citation.spage | 1271 | |
dc.citation.epage | 1280 | |
dc.type | article | |
dc.language.iso | en | |
dc.publisher | Institute of Electrical and Electronics Engineers Inc. | |
dc.identifier.doi | 10.1109/TEVC.2022.3204412 | |
dc.relation.uri | https://ieeexplore.ieee.org/document/9878135 | |
dc.subject | benchmark testing | en |
dc.subject | benchmark testing | en |
dc.subject | boundary control method | en |
dc.subject | computational intelligence | en |
dc.subject | evolutionary computation | en |
dc.subject | metaheuristics | en |
dc.subject | optimization | en |
dc.subject | performance evaluation | en |
dc.subject | reflection | en |
dc.subject | symbols | en |
dc.subject | task analysis | en |
dc.subject | tutorials | en |
dc.description.abstract | Benchmarking various metaheuristics and their new enhancements, strategies, and adaptation mechanisms has become standard in computational intelligence research. Recently, many challenges and issues regarding fair comparisons and recommendations toward good practices for benchmarking of metaheuristic algorithms, have been identified. This article is aimed at an important issues in metaheuristics design and benchmarking, which are boundary strategies or boundary control methods (BCMs). This work aims to investigate whether the choice of a BCM could significantly influence the performance of competitive algorithms. The experiments encompass the top three performing algorithms from IEEE CEC competitions 2017 and 2020 with six different BCMs. We provide extensive statistical analysis and rankings resulting in conclusions and recommendations for metaheuristics researchers and possibly also for the future direction of benchmark definitions. We conclude that the BCM should be considered another vital metaheuristics input variable for unambiguous reproducibility of results in benchmarking and for a better understanding of population dynamics, since the BCM setting could impact the optimization method performance. | en |
utb.faculty | Faculty of Applied Informatics | |
dc.identifier.uri | http://hdl.handle.net/10563/1011139 | |
utb.identifier.obdid | 43883627 | |
utb.identifier.scopus | 2-s2.0-85137939631 | |
utb.identifier.wok | 000892933300008 | |
utb.identifier.coden | ITEVF | |
utb.source | j-scopus | |
dc.date.accessioned | 2022-09-30T08:34:14Z | |
dc.date.available | 2022-09-30T08:34:14Z | |
dc.description.sponsorship | Internal Grant Agency of Tomas Bata University [IGA/CebiaTech/2022/001] | |
utb.contributor.internalauthor | Kadavý, Tomáš | |
utb.contributor.internalauthor | Viktorin, Adam | |
utb.contributor.internalauthor | Kazíková, Anežka | |
utb.contributor.internalauthor | Pluháček, Michal | |
utb.contributor.internalauthor | Šenkeřík, Roman | |
utb.fulltext.affiliation | Tomas Kadavy, Adam Viktorin, Anezka Kazikova, Michal Pluhacek, Member, IEEE, and Roman Senkerik, Member, IEEE The authors are with the Faculty of Applied Informatics, Tomas Bata University in Zlin, nam. T. G. Masaryka 5555, Zlin 76001, Czech Republic {kadavy, aviktorin, kazikova, pluhacek, senkerik}@utb.cz https://ailab.fai.utb.cz/ | |
utb.fulltext.dates | Manuscript received September 15, 2021 | |
utb.fulltext.references | [1] W. Wong and C. I. Ming, “A review on metaheuristic algorithms: recent trends, benchmarking and applications,” in 2019 7th International Conference on Smart Computing & Communications (ICSCC). IEEE, 2019, pp. 1–5. [2] G. R. Raidl, “A unified view on hybrid metaheuristics,” in International workshop on hybrid metaheuristics. Springer, 2006, pp. 1–12. [3] A. Kazikova, M. Pluhacek, and R. Senkerik, “Why tuning the control parameters of metaheuristic algorithms is so important for fair comparison?” MENDEL, vol. 26, no. 2, pp. 9–16, Dec. 2020. [Online]. Available: http://ib-b2b.test.infv.eu/index.php/mendel/article/view/120 [4] ——, “How does the number of objective function evaluations impact our understanding of metaheuristics behavior?” IEEE Access, vol. 9, pp. 44 032–44 048, 2021. [5] A. Wagdy, A. A. Hadi, A. K. Mohamed, P. Agrawal, A. Kumar, and P. N. Suganthan, “Problem definitions and evaluation criteria for the cec 2021 special session and competition on single objective bound constrained numerical optimization.” Technical Report, Nanyang Technological University, Singapore, 2020. [6] N. Hansen, A. Auger, R. Ros, O. Mersmann, T. Tušar, and D. Brockhoff, “Coco: A platform for comparing continuous optimizers in a black-box setting,” Optimization Methods and Software, vol. 36, no. 1, pp. 114–144, 2021. [7] T. Bartz-Beielstein, C. Doerr, J. Bossek, S. Chandrasekaran, T. Eftimov, A. Fischbach, P. Kerschke, M. Lopez-Ibanez, K. M. Malan, J. H. Moore, B. Naujoks, P. Orzechowski, V. Volz, M. Wagner, and T. Weise, “Benchmarking in optimization: Best practice and open issues,” 2020. [8] A. LaTorre, D. Molina, E. Osaba, J. Poyatos, J. Del Ser, and F. Herrera, “A prescription of methodological guidelines for comparing bio-inspired optimization algorithms,” Swarm and Evolutionary Computation, p. 100973, 2021. [9] T. Kadavy, M. Pluhacek, A. Viktorin, and R. Senkerik, “Comparing strategies for search space boundaries violation in pso,” in International Conference on Artificial Intelligence and Soft Computing. Springer, 2017, pp. 655–664. [10] ——, “Boundary strategies for firefly algorithm analysed using cec’17 benchmark.” in ECMS, 2018, pp. 170–175. [11] T. Kadavy, M. Pluhacek, R. Senkerik, and A. Viktorin, “Boundary strategies for self-organizing migrating algorithm analyzed using cec’17 benchmark,” in Swarm, Evolutionary, and Memetic Computing and Fuzzy and Neural Computing. Springer, 2019, pp. 58–69. [12] S. Helwig, J. Branke, and S. Mostaghim, “Experimental analysis of bound handling techniques in particle swarm optimization,” IEEE Transactions on Evolutionary computation, vol. 17, no. 2, pp. 259–271, 2012. [13] E. T. Oldewage, A. P. Engelbrecht, and C. W. Cleghorn, “Boundary constraint handling techniques for particle swarm optimization in high dimensional problem spaces,” in International Conference on Swarm Intelligence. Springer, 2018, pp. 333–341. [14] M. Clerc, “Confinements and biases in particle swarm optimisation,” 2006. [15] W.-J. Zhang, X.-F. Xie, and D.-C. Bi, “Handling boundary constraints for numerical optimization by particle swarm flying in periodic search space,” in Proceedings of the 2004 Congress on Evolutionary Computation (IEEE Cat. No. 04TH8753), vol. 2. IEEE, 2004, pp. 2307–2311. [16] Z. Michalewicz and M. Schoenauer, “Evolutionary algorithms for constrained parameter optimization problems,” Evolutionary computation, vol. 4, no. 1, pp. 1–32, 1996. [17] S. Koziel and Z. Michalewicz, “Evolutionary algorithms, homomorphous mappings, and constrained parameter optimization,” Evolutionary computation, vol. 7, no. 1, pp. 19–44, 1999. [18] M. Sanaz, H. Werner, and W. Anja, “Linear multi-objective particle swarm optimization,” in Stigmergic Optimization. Springer, 2006, pp. 209–238. [19] N. Hansen, “The cma evolution strategy: A tutorial,” arXiv preprint arXiv:1604.00772, 2016. [20] J. de Nobel, D. Vermetten, H. Wang, C. Doerr, and T. Bäck, Tuning as a Means of Assessing the Benefits of New Ideas in Interplay with Existing Algorithmic Modules. New York, NY, USA: Association for Computing Machinery, 2021, p. 1375–1384. [Online]. Available: https://doi.org/10.1145/3449726.3463167 [21] J. Ronkkonen, S. Kukkonen, and K. V. Price, “Real-parameter optimiza- tion with differential evolution,” in 2005 IEEE congress on evolutionary computation, vol. 1. IEEE, 2005, pp. 506–513. [22] J. Brest, V. Zumer, and M. S. Maucec, “Self-adaptive differential evolution algorithm in constrained real-parameter optimization,” in 2006 IEEE international conference on evolutionary computation. IEEE, 2006, pp. 215–222. [23] K. Price, R. M. Storn, and J. A. Lampinen, Differential evolution: a practical approach to global optimization. Springer Science & Business Media, 2006. [24] S.-M. Guo, J. S.-H. Tsai, C.-C. Yang, and P.-H. Hsu, “A self-optimization approach for l-shade incorporated with eigenvector-based crossover and successful-parent-selecting framework on cec 2015 benchmark set,” in 2015 IEEE congress on evolutionary computation (CEC). IEEE, 2015, pp. 1003–1010. [25] J. Zhang and A. C. Sanderson, “Jade: adaptive differential evolution with optional external archive,” IEEE Transactions on evolutionary computation, vol. 13, no. 5, pp. 945–958, 2009. [26] F. Caraffini, A. V. Kononova, and D. Corne, “Infeasibility and structural bias in differential evolution,” Information Sciences, vol. 496, pp. 161–179, 2019. [27] R. Boks, A. V. Kononova, and H. Wang, Quantifying the Impact of Boundary Constraint Handling Methods on Differential Evolution. New York, NY, USA: Association for Computing Machinery, 2021, p. 1199–1207. [Online]. Available: https://doi.org/10.1145/3449726.3463214 [28] F. Marini and B. Walczak, “Particle swarm optimization (pso). a tutorial,” Chemometrics and Intelligent Laboratory Systems, vol. 149, pp. 153–165, 2015. [29] A. E. Eiben, J. E. Smith et al., Introduction to evolutionary computing. Springer, 2003. [30] X.-S. Yang, “Firefly algorithm, stochastic test functions and design optimisation,” International journal of bio-inspired computation, vol. 2, no. 2, pp. 78–84, 2010. [31] ——, “Firefly algorithms for multimodal optimization,” in International symposium on stochastic algorithms. Springer, 2009, pp. 169–178. [32] X.-S. Yang and S. Deb, “Engineering optimisation by cuckoo search,” International Journal of Mathematical Modelling and Numerical Optimisation, vol. 1, no. 4, pp. 330–343, 2010. [33] R. Mallipeddi, P. N. Suganthan, Q.-K. Pan, and M. F. Tasgetiren, “Differential evolution algorithm with ensemble of parameters and mutation strategies,” Applied soft computing, vol. 11, no. 2, pp. 1679–1696, 2011. [34] J. Riget and J. S. Vesterstrøm, “A diversity-guided particle swarm optimizer-the arpso,” Dept. Comput. Sci., Univ. of Aarhus, Aarhus, Denmark, Tech. Rep, vol. 2, p. 2002, 2002. [35] P. Kora and K. S. R. Krishna, “Hybrid firefly and particle swarm optimization algorithm for the detection of bundle branch block,” International Journal of the Cardiovascular Academy, vol. 2, no. 1, pp. 44–48, 2016. [36] N. Awad, M. Ali, J. Liang, B. Qu, and P. Suganthan, “Problem definitions and evaluation criteria for the cec 2017 special sessionand competition on single objective real-parameter numerical optimization. nanyang technologial university, jordan university of science and technology and zhengzhou university, singapore and zhenzhou,” Nanyang Technological University, Jordan University of Science and Technology and Zhengzhou University, Singapore and Zhenzhou, China, Tech. Rep, vol. 201611, 2016. [37] P. N. Suganthan, N. Hansen, J. J. Liang, K. Deb, Y.-P. Chen, A. Auger, and S. Tiwari, “Problem definitions and evaluation criteria for the cec 2005 special session on real-parameter optimization,” KanGAL report, vol. 2005005, no. 2005, p. 2005, 2005. [38] K. Price, N. Awad, M. Ali, and P. Suganthan, “Problem definitions and evaluation criteria for the 100-digit challenge special session and competition on single objective numerical optimization,” in Technical Report. Nanyang Technological University, 2018. [39] C. T. Yue, K. V. Price, P. N. Suganthan, J. J. Liang, M. Z. Ali, B. Y. Qu, N. H. Awad, and P. P. Biswas, “Problem definitions and evaluation criteria for the cec 2020 special session and competition on single objective bound constrained numerical optimization,” Technical Report 201911, 2019. [40] N. H. Awad, M. Z. Ali, J. J. Liang, B. Y. Qu, and P. N. Suganthan, “Cec 2017 special session on single objective numerical optimization single bound constrained real-parameter numerical optimization,” Jul 2019. [Online]. Available: https://github.com/P-N-Suganthan/CEC2017-BoundContrained/blob/master/Bound-Constrained-Comparisons.pdf [41] A. Kumar, R. K. Misra, and D. Singh, “Improving the local search capability of effective butterfly optimizer using covariance matrix adapted retreat phase,” in 2017 IEEE congress on evolutionary computation (CEC). IEEE, 2017, pp. 1835–1842. [42] N. H. Awad, M. Z. Ali, and P. N. Suganthan, “Ensemble sinusoidal differential covariance matrix adaptation with euclidean neighborhood for solving cec2017 benchmark problems,” in 2017 IEEE Congress on Evolutionary Computation (CEC). IEEE, 2017, pp. 372–379. [43] J. Brest, M. S. Maučec, and B. Bošković, “Single objective real- parameter optimization: Algorithm jso,” in 2017 IEEE congress on evolutionary computation (CEC). IEEE, 2017, pp. 1311–1318. [44] D. Jagodziński and J. Arabas, “A differential evolution strategy,” in 2017 IEEE Congress on Evolutionary Computation (CEC). IEEE, 2017, pp. 1872–1876. [45] D. Maharana, R. Kommadath, and P. Kotecha, “Dynamic yin-yang pair optimization and its performance on single objective real parameter problems of cec 2017,” in 2017 IEEE Congress on Evolutionary Computation (CEC). IEEE, 2017, pp. 2390–2396. [46] P. Bujok and J. Tvrdík, “Enhanced individual-dependent differential evolution with population size adaptation,” in 2017 IEEE congress on evolutionary computation (CEC). IEEE, 2017, pp. 1358–1365. [47] A. W. Mohamed, A. A. Hadi, A. M. Fattouh, and K. M. Jambi, “Lshade with semi-parameter adaptation hybrid with cma-es for solving cec 2017 benchmark problems,” in 2017 IEEE Congress on evolutionary computation (CEC). IEEE, 2017, pp. 145–152. [48] K. M. Sallam, S. M. Elsayed, R. A. Sarker, and D. L. Essam, “Multi-method based orthogonal experimental design algorithm for solving cec2017 competition problems,” in 2017 IEEE Congress on Evolutionary Computation (CEC). IEEE, 2017, pp. 1350–1357. [49] A. LaTorre and J.-M. Peña, “A comparison of three large-scale global optimizers on the cec 2017 single objective real parameter numerical optimization benchmark,” in 2017 IEEE Congress on Evolutionary Computation (CEC). IEEE, 2017, pp. 1063–1070. [50] A. Tangherloni, L. Rundo, and M. S. Nobile, “Proactive particles in swarm optimization: A settings-free algorithm for real-parameter single objective optimization problems,” in 2017 IEEE Congress on Evolutionary Computation (CEC). IEEE, 2017, pp. 1940–1947. [51] R. Biedrzycki, “A version of ipop-cma-es algorithm with midpoint for cec 2017 single objective bound constrained problems,” in 2017 IEEE Congress on Evolutionary Computation (CEC). IEEE, 2017, pp. 1489–1494. [52] R. Kommadath and P. Kotecha, “Teaching learning based optimization with focused learning and its performance on cec2017 functions,” in 2017 IEEE congress on evolutionary computation (CEC). IEEE, 2017, pp. 2397–2403. [53] R. Tanabe and A. S. Fukunaga, “Improving the search performance of shade using linear population size reduction,” in 2014 IEEE congress on evolutionary computation (CEC). IEEE, 2014, pp. 1658–1665. [54] N. H. Awad, M. Z. Ali, P. N. Suganthan, and R. G. Reynolds, “An ensemble sinusoidal parameter adaptation incorporated with l-shade for solving cec2014 benchmark problems,” in 2016 IEEE congress on evolutionary computation (CEC). IEEE, 2016, pp. 2958–2965. [55] C. T. Yue, K. V. Price, P. N. Suganthan, J. J. Liang, M. Z. Ali, B. Y. Qu, N. H. Awad, and P. P. Biswas, “Competition on single objective bound constrained numerical optimization,” Sep 2020. [Online]. Available: https://github.com/P-N-Suganthan/2020-Bound-Constrained-Opt-Benchmark/blob/master/CEC2020 BCC Results Analysis_R_Aug.16.pdf [56] K. M. Sallam, S. M. Elsayed, R. K. Chakrabortty, and M. J. Ryan, “Improved multi-operator differential evolution algorithm for solving unconstrained problems,” in 2020 IEEE Congress on Evolutionary Computation (CEC). IEEE, 2020, pp. 1–8. [57] A. W. Mohamed, A. A. Hadi, A. K. Mohamed, and N. H. Awad, “Evaluating the performance of adaptive gainingsharing knowledge based algorithm on cec 2020 benchmark problems,” in 2020 IEEE Congress on Evolutionary Computation (CEC). IEEE, 2020, pp. 1–8. [58] J. Brest, M. S. Maučec, and B. Bošković, “Differential evolution algorithm for single objective bound-constrained optimization: Algorithm j2020,” in 2020 IEEE Congress on Evolutionary Computation (CEC). IEEE, 2020, pp. 1–8. [59] R. Salgotra, U. Singh, S. Saha, and A. H. Gandomi, “Improving cuckoo search: Incorporating changes for cec 2017 and cec 2020 benchmark problems,” in 2020 IEEE Congress on Evolutionary Computation (CEC). IEEE, 2020, pp. 1–7. [60] A. Bolufé-Röhler and S. Chen, “A multi-population exploration-only exploitation-only hybrid on cec-2020 single objective bound constrained problems,” in 2020 IEEE Congress on Evolutionary Computation (CEC). IEEE, 2020, pp. 1–8. [61] V. Stanovov, S. Akhmedova, and E. Semenkin, “Ranked archive differential evolution with selective pressure for cec 2020 numerical optimization,” in 2020 IEEE Congress on Evolutionary Computation (CEC). IEEE, 2020, pp. 1–7. [62] A. Viktorin, R. Senkerik, M. Pluhacek, T. Kadavy, and A. Zamuda, “Dish-xx solving cec2020 single objective bound constrained numerical optimization benchmark,” in 2020 IEEE Congress on Evolutionary Computation (CEC). IEEE, 2020, pp. 1–8. [63] P. Bujok, P. Kolenovsky, and V. Janisch, “Eigenvector crossover in jde100 algorithm,” in 2020 IEEE Congress on Evolutionary Computation (CEC). IEEE, 2020, pp. 1–6. [64] P. P. Biswas and P. N. Suganthan, “Large initial population and neighborhood search incorporated in lshade to solve cec2020 benchmark problems,” in 2020 IEEE Congress on Evolutionary Computation (CEC). IEEE, 2020, pp. 1–7. [65] Y.-C. Jou, S.-Y. Wang, J.-F. Yeh, and T.-C. Chiang, “Multi-population modified l-shade for single objective bound constrained optimization,” in 2020 IEEE Congress on Evolutionary Computation (CEC). IEEE, 2020, pp. 1–8. [66] T. Kadavy, M. Pluhacek, A. Viktorin, and R. Senkerik, “Self-organizing migrating algorithm with clustering-aided migration,” in Proceedings of the 2020 Genetic and Evolutionary Computation Conference Companion, ser. GECCO ’20. New York, NY, USA: Association for Computing Machinery, 2020, p. 1441–1447. [Online]. Available: https://doi.org/10.1145/3377929.3398129 [67] A. W. Mohamed, A. A. Hadi, and A. K. Mohamed, “Gaining-sharing knowledge based algorithm for solving optimization problems: a novel nature-inspired algorithm,” International Journal of Machine Learning and Cybernetics, vol. 11, no. 7, pp. 1501–1529, 2020. [68] J. Brest, S. Greiner, B. Boskovic, M. Mernik, and V. Zumer, “Self-adapting control parameters in differential evolution: A comparative study on numerical benchmark problems,” IEEE transactions on evolutionary computation, vol. 10, no. 6, pp. 646–657, 2006. [69] J. Brest, M. S. Maučec, and B. Bošković, “The 100-digit challenge: Algorithm jde100,” in 2019 IEEE Congress on Evolutionary Computation (CEC). IEEE, 2019, pp. 19–26. [70] M. Friedman, “The use of ranks to avoid the assumption of normality implicit in the analysis of variance,” Journal of the american statistical association, vol. 32, no. 200, pp. 675–701, 1937. | |
utb.fulltext.sponsorship | This work was supported by the Internal Grant Agency of Tomas Bata University under the Projects no. IGA/CebiaTech/2022/001, and further by the resources of A.I.Lab at the Faculty of Applied Informatics, Tomas Bata University in Zlin (ailab.fai.utb.cz). | |
utb.wos.affiliation | [Kadavy, Tomas; Viktorin, Adam; Kazikova, Anezka; Pluhacek, Michal; Senkerik, Roman] Tomas Bata Univ Zlin, Fac Appl Informat, Zlin 76001, Czech Republic | |
utb.scopus.affiliation | Faculty of Applied Informatics, Tomas Bata University in Zlin, nam. T. G. Masaryka, Zlin, Czech Republic | |
utb.fulltext.projects | IGA/CebiaTech/2022/001 | |
utb.fulltext.faculty | Faculty of Applied Informatics | |
utb.fulltext.ou | - | |
utb.identifier.jel | - |