Abstract
AbstractTo develop new meta-heuristic algorithms and evaluate on the benchmark functions is the most challenging task. In this paper, performance of the various developed meta-heuristic algorithms are evaluated on the recently developed CEC 2021 benchmark functions. The objective functions are parametrized by inclusion of the operators, such as bias, shift and rotation. The different combinations of the binary operators are applied to the objective functions which leads to the CEC2021 benchmark functions. Therefore, different meta-heuristic algorithms are considered which solve the benchmark functions with different dimensions. The performance of some basic, advanced meta-heuristics algorithms and the algorithms that participated in the CEC2021 competition have been experimentally investigated and many observations, recommendations, conclusions have been reached. The experimental results show the performance of meta-heuristic algorithms on the different combinations of binary parameterized operators.
Publisher
Springer Science and Business Media LLC
Subject
Artificial Intelligence,Software
Reference65 articles.
1. Michalewicz Z, Dasgupta D, Le Riche RG, Schoenauer M (1996) Evolutionary algorithms for constrained engineering problems. Comput Ind Eng 30(4):851–870
2. Gusel L, Rudolf R, Brezocnik M (2015) Genetic based approach to predicting the elongation of drawn alloy. Int J Simul Modell 14(1):39–47
3. Zhang J, Zhan Z-H, Lin Y, Chen N, Gong Y-J, Zhong J-H, Chung HS, Li Y, Shi Y-H (2011) Evolutionary computation meets machine learning: a survey. IEEE Comput Intell Mag 6(4):68–75
4. Collange G, Delattre N, Hansen N, Quinquis I, Schoenauer M (2010) Multidisciplinary optimization in the design of future space launchers
5. Mora AM, Squillero G (2015) Applications of evolutionary computation. In: Proceedings 18th European Conference, EvoApplications 2015, Copenhagen, Denmark, April 8-10, 2015,. Springer, vol. 9028
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