Campelo, F. & Aranha, C. Evolutionary computation bestiary. https://github.com/fcampelo/EC-Bestiary (accessed 7 February 2022).
Weyland, D. A rigorous analysis of the harmony search algorithm: how the research community can be misled by a novel methodology. Int. J. Appl. Metaheuristic Comput. 12, 50–60 (2010).
Camacho Villalón, C. L., Dorigo, M. & Stützle, T. The intelligent water drops algorithm: why it cannot be considered a novel algorithm. Swarm Intell. 13, 173–192 (2019).
Camacho Villalón, C. L., Stützle, T. & Dorigo, M. Grey wolf, firefly and bat algorithms: three widespread algorithms that do not contain any novelty. In Int. Conference on Swarm Intelligence 121–133 (Springer, 2020).
Camacho Villalón, C. L., Stützle, T. & Dorigo, M. Cuckoo Search ≡ μ+λ – Evolution Strategy — A Rigorous Analysis of an Algorithm that has Been Misleading the Research Community for More Than 10 Years and Nobody Seems to have Noticed TR/IRIDIA/2021-006 (IRIDIA, Université Libre de Bruxelles, 2021).
Piotrowski, A. P., Napiorkowski, J. J. & Rowinski, P. M. How novel is the “novel” black hole optimization approach? Inf. Sci. 267, 191–200 (2014).
Aranha, C. et al. Metaphor‑based metaheuristics, a call for action: the elephant in the room. Swarm Intell. 16, 1–6 (2022).
Hellwig, M. & Beyer, H. G. Benchmarking evolutionary algorithms for single objective real-valued constrained optimization – a critical review. Swarm Evol. Comput. 44, 927–944 (2019).
Garcia-Martinez, C., Gutierrez, P. D., Molina, D., Lozano, M. & Herrera, F. Since CEC 2005 competition on real-parameter optimisation: a decade of research, progress and comparative analysis’s weakness. Soft Comput. 21, 5573–5583 (2017).
Hansen, N., Auger, A., Mersmann, O., Tuvar, T. & Brockhoff, D. COCO: a platform for comparing continuous optimizers in a black-box setting. Preprint at https://arxiv.org/abs/1603.08785 (2016).
Suganthan, N. P. Github repository of CEC competitions. GitHub https://github.com/P-N-Suganthan (2022).
Garden, R. W. & Engelbrecht, A. P. Analysis and classification of optimization benchmark functions and benchmark suites. In IEEE Congress on Evolutionary Computation 1664–1669 (2014).
COCO Data Archives (2022); https://numbbo.github.io/data-archive/
Piotrowski, A. P. Regarding the rankings of optimization heuristics based on artificially-constructed benchmark functions. Inf. Sci. 297, 191–201 (2015).
Tzanetos, A. & Dounias, G. Nature inspired optimization algorithms or simply variations of metaheuristics? Artif. Intell. Rev. 54, 1841–1862 (2021).
Kumar, A., Suganthan, P. N., Mohamed, A. W., Hadi, A. A. & Mohamed, A. K. Special session & competitions on single objective bound constrained numerical optimization. In IEEE Congress on Evolutionary Computation (IEEE, 2021).
Niu, P., Niu, S., Liu, N. & Chang, L. The defect of the Grey Wolf optimization algorithm and its verification method. Knowl.-Based Syst. 171, 37–43 (2019).
Castelli, M., Manzoni, L., Mariot, L., Nobile, M. S. & Tangherloni, A. Salp Swarm Optimization: a critical review. Expert Syst. Appl. 189, 116029 (2022).
Kudela, J. Commentary on: “STOA: A bio-inspired based optimization algorithm for industrial engineering problems” [EAAI, 82 (2019), 148–174] and “Tunicate Swarm Algorithm: A new bio-inspired based metaheuristic paradigm for global optimization” [EAAI, 90 (2020), no. 103541]. Eng. Appl. Artif. Intell. 113, 104930 (2022).
Suyanto, S., Ariyanto, A. A. & Ariyanto, A. F. Komodo Mlipir Algorithm. Appl. Soft Comput. 114, 108043 (2022).
Li, S., Chen, H., Wang, M., Heidari, A. A. & Mirjalili, S. Slime mould algorithm: a new method for stochastic optimization. Future Gener. Comput. Syst. 111, 300–323 (2020).
Arora, S. & Singh, S. Butterfly optimization algorithm: a novel approach for global optimization. Soft Comput. 23, 715–734 (2019).
Ahmadianfar, I., Bozorg-Haddad, O. & Chu, X. Gradient-based optimizer: a new metaheuristic optimization algorithm. Inf. Sci. 540, 131–159 (2020).
Oszust, M. Enhanced marine predators algorithm with local escaping operator for global optimization. Knowl.-Based Syst. 232, 107467 (2021).
Heidari, A. A. et al. Harris hawks optimization: algorithm and applications. Future Gener. Comput. Syst. 97, 849–872 (2019).
Dhiman, G. & Kaur, A. STOA: a bio-inspired based optimization algorithm for industrial engineering problems. Eng. Appl. Artif. Intell. 82, 148–174 (2019).
Tanabe, R. & Fukunaga, A. Improving the search performance of SHADE using linear population size reduction. In 2014 IEEE Congress on Evolutionary Computation 1658–1665 (IEEE, 2014).
Zhang, G. & Shi, Y. Hybrid sampling evolution strategy for solving single objective bound constrained problems. In 2018 IEEE Congress on Evolutionary Computation (IEEE, 2018).
Fister, I. et al. On selection of a benchmark by determining the algorithms’ qualities. IEEE Access 9, 51166–51178 (2021).
CodeOcean Capsule (2022); https://doi.org/10.24433/CO.1268126.v1
Bayzidi, H., Talatahari, S., Saraee, M. & Lamarche, C.-P. Social network search for solving engineering optimization problems. Comput. Intell. Neurosc. 9, 8548639 (2021).
Kudela, J. & Matousek, R. New benchmark functions for single-objective optimization based on a zigzag pattern. IEEE Access 10, 8262–8278 (2022).
Vecek, N., Crepinsek, M., Mernik, M. & Hrncic, D. A comparison between different chess rating systems for ranking evolutionary algorithms. In 2014 Federated Conference on Computer Science and Information Systems 511–518 (IEEE, 2014).
Del Ser, J. et al. More is not always better: insights from a massive comparison of meta-heuristic algorithms over real-parameter optimization problems. In IEEE Symposium Series on Computational Intelligence (IEEE, 2021).
Scipy benchmark functions. GitHub https://github.com/scipy/scipy/tree/main/benchmarks/benchmarks/go_benchmark_functions (2022).
Tzanetos, A. & Dounias, G. A comprehensive survey on the applications of swarm intelligence and bio-inspired evolutionary strategies. Mach. Learn. Paradigms 18, 337–378 (2020).
Gleixner, A. et al. MIPLIB 2017: data-driven compilation of the 6th mixed-integer programming library. Math. Program. Comput. 13, 443–490 (2021).
Mohamed, A.W. et al. Problem Definitions and Evaluation Criteria for the CEC 2021 Special Session and Competition on Single Objective Bound Constrained Numerical Optimization (Cairo University, 2020).
Yue, C.T. et al. Problem Definitions and Evaluation Criteria for the CEC 2020 Special Session and Competition on Single Objective Bound Constrained Numerical Optimization Technical report 201911 (Computational Intelligence Laboratory, Zhengzhou University, 2019).
Kudela, J. Novel zigzag-based benchmark functions for bound constrained single objective optimization. In 2021 IEEE Congress on Evolutionary Computation (IEEE, 2021).
Vecek, N., Crepinsek, M. & Mernik, M. On the influence of the number of algorithms, problems, and independent runs in the comparison of evolutionary algorithms. Appl. Soft Comput. 54, 23–45 (2017).
Osaba, E. et al. A tutorial on the design, experimentation and application of metaheuristic algorithms to real-world optimization problems. Swarm Evol. Comput. 64, 100888 (2021).
Doerr, C., Wang, H., Ye, F., van Rijn, S. & Back, T. IOHprofiler: a benchmarking and profiling tool for iterative optimization heuristics. Preprint at https://arxiv.org/abs/1810.05281 (2018).
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