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Incremental gravitational search algorithm for high-dimensional benchmark functions

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Abstract

In this study, gravitational search algorithm (GSA), which is a powerful optimization algorithm developed in recent years and based on physics, is improved by integrating the incremental social learning structure. In this improvement, new agents have been added to the GSA starting from the first population at certain steps, agent insertion on the maximum population number has been terminated, and the search has been continued until the desired function call is accomplished. This improved algorithm, which is a recent version of the GSA, is named as the incremental gravitational search algorithm (IGSA). The process of adding agent to the population has been performed with three different approaches. Results of the 30-dimensional test functions, which are solved by the GSA in the literature, are compared with the obtained results of IGSA, developed for each approach. Thereafter, the dimensions of the same test functions have been increased (50 and 100 dimensions) and resolved with IGSA, and the results are discussed.

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Acknowledgements

This work was supported by the Dumlupınar University (Turkey) Scientific Research Projects Commission (BAP) under the 2016-65 Project Number.

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Correspondence to Serdar Özyön.

Appendix

Appendix

See Tables 13, 14, 15, 16, and 17.

Table 13 Coefficients aij in function f14(x)
Table 14 Coefficients ai and bi in function f15(x)
Table 15 Coefficients aij, ci and Pij in function f19(x)
Table 16 Coefficients aij, ci and Pij in function f20(x)
Table 17 Coefficients aij and ci in function f21(x), f22(x) and f23(x)

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Özyön, S., Yaşar, C. & Temurtaş, H. Incremental gravitational search algorithm for high-dimensional benchmark functions. Neural Comput & Applic 31, 3779–3803 (2019). https://doi.org/10.1007/s00521-017-3334-8

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