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A Novel Genetic Algorithm for Global Optimization

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Abstract

This paper presents a novel genetic algorithm for globally solving un-constraint optimization problem. In this algorithm, a new real coded crossover operator is proposed firstly. Furthermore, for improving the convergence speed and the searching ability of our algorithm, the good point set theory rather than random selection is used to generate the initial population, and the chaotic search operator is adopted in the best solution of the current iteration. The experimental results tested on numerical benchmark functions show that this algorithm has excellent solution quality and convergence characteristics, and performs better than some algorithms.

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Author information

Authors and Affiliations

  1. College of Mathematics and Information, Henan Normal University, Xinxiang, 453007, China

    Chun-feng Wang, Kui Liu & Pei-ping Shen

  2. Henan Engineering Laboratory for Big Data Statistical Analysis and Optimal Control, School of Mathematics and Information Sciences, Henan Normal University, Xinxiang, 453007, China

    Chun-feng Wang

Authors
  1. Chun-feng Wang
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  2. Kui Liu
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  3. Pei-ping Shen
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Corresponding author

Correspondence to Chun-feng Wang.

Additional information

This paper is supported by the National Natural Science Foundation NSFC(11671122); the Key Project of Henan Educational Committee (19A110021,19A510014).

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Wang, Cf., Liu, K. & Shen, Pp. A Novel Genetic Algorithm for Global Optimization. Acta Math. Appl. Sin. Engl. Ser. 36, 482–491 (2020). https://doi.org/10.1007/s10255-020-0930-7

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  • DOI: https://doi.org/10.1007/s10255-020-0930-7

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