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Elite-based feedback boosted artificial rabbits-inspired optimizer with mutation and adaptive group: a case study of degree reduction for ball NURBS curves

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Abstract

The approximate degree reduction of ball NURBS curves is a knotty technique in geometric modeling. As is known to all, the degree reduction of ball NURBS ones is mathematically an optimization problem that can be solved efficiently by swarm intelligence algorithm. In this paper, an improved artificial rabbits optimization (ARO) is used to accomplish the optimal multi-degree reduction of the ball curves. Firstly, by incorporating mutation strategy, adaptive group strategy and Elite-feedback strategy to the ARO, the improved ARO named IARO is developed to increase the population diversity and enhance its capability of jumping out of the local minima. Secondly, the superiority of IARO is comprehensively verified by comparing with the original ARO and numerous celebrated and newly developed algorithms on the IEEE Congress on Evolutionary Computation (CEC, for short) 2017 benchmark functions. Meanwhile, the statistical testing of IARO has been conducted to validate its significance. Finally, by minimizing the distance between the original curve and the approximate curve, the optimization models of multi-degree reduction for ball NURBS curves are established. The IARO is utilized to solve the optimization models, and the optimal approximate ball NURBS curves are obtained. Some representative numerical examples illustrate the ability of the proposed IARO in effectively solving the multi-degree reduction problem of ball NURBS curves in terms of precision, robustness, and convergence characteristics.

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Funding

This work is supported by the National Natural Science Foundation of China (Grant No. 51875454).

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Authors and Affiliations

  1. Department of Applied Mathematics, Xi’an University of Technology, Xi’an, 710054, People’s Republic of China

    Gang Hu & Wenlong Jing

  2. School of Computer Science and Engineering, Xi’an University of Technology, Xi’an, 710048, People’s Republic of China

    Gang Hu

  3. Faculty of Computers and Information, Minia University, Minia, Egypt

    Essam H. Houssein

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  1. Gang Hu
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  2. Wenlong Jing
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Correspondence to Gang Hu.

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Appendix

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Table 7 The comparison results of the three improvement strategies of ARO on CEC2017 (MARO corresponding mutation strategy, EARO corresponds to elite strategy, GARO corresponds to adaptive group strategy, IARO corresponds to all three strategies)
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7

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Hu, G., Jing, W. & Houssein, E.H. Elite-based feedback boosted artificial rabbits-inspired optimizer with mutation and adaptive group: a case study of degree reduction for ball NURBS curves. Soft Comput 27, 16919–16957 (2023). https://doi.org/10.1007/s00500-023-09023-w

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