Abstract
In this paper, a new method of deformed stars for global optimization based on the ideas and principles of the evolutionary paradigm was proposed. The two-dimensional case was developed and then extended for n-dimensional case. This method is based on the assumption of rational use of potential solutions groups, which allows increasing the rate of convergence and the accuracy of result. Populations of potential solutions are used to optimize the multivariable function, as well as their transformation, the operations of deformation, rotation and compression. The obtained results of experiments allow us to conclude that the proposed method is applicable to solving problems of finding optimal (suboptimal) values, including non-differentiated functions. The advantages of the developed method in comparison of genetic algorithms, evolutionary strategies and differential evolution as the most typical evolutionary algorithms were shown. The experiments were conducted using several well-known functions for global optimization (Ackley’s function, Rosenbrock’s saddle, Rastrigin’s function).
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Snytyuk, V., Antonevych, M., Didyk, A., Tmienova, N. (2022). The Method of Deformed Stars as a Population Algorithm for Global Optimization. In: Zgurovsky, M., Pankratova, N. (eds) System Analysis & Intelligent Computing. SAIC 2020. Studies in Computational Intelligence, vol 1022. Springer, Cham. https://doi.org/10.1007/978-3-030-94910-5_13
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DOI: https://doi.org/10.1007/978-3-030-94910-5_13
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