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Heterogeneous ensemble algorithms for function optimization

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A Correction to this article was published on 23 June 2022

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Abstract

Ensemble optimization algorithms have been widely used in solving complex optimization problems because they can make full use of the advantages of different algorithms or strategies. However, it is very difficult to integrate multiple optimization methods with different structures in an algorithm framework because evolutionary mechanisms or coding methods are different in different evolutionary computation (EC) algorithms. To give full play to the complementary advantages of different algorithms without obviously increasing the computational complexity of the algorithm, a framework of heterogeneous ensemble algorithms (EHA) with multiple populations is developed. First, to maintain the diversities of the subpopulations, a new grouping strategy considering the convergence accuracy and acceleration is designed to divide the population into three subpopulations. This can decrease the probability of local convergence of the algorithm. Second, three algorithms considering the characteristics of the individuals are distributed for the subpopulations to balance the exploitation and exploration abilities of the framework. Third, a method for chromosome modification is designed to realize the smooth evolution of individuals in different subpopulations. Finally, the individuals are regrouped to realize the information exchange among three subpopulations. The performance of EHA is evaluated on two data suites (IEEE CEC2005 and IEEE CEC2014), and the results are compared with those of some other algorithms. The results indicate that EHA has excellent optimization performance.

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Funding

This work is partially supported by the National Natural Science Foundation of China (Grant No 61976101, 62006091, 62006092). This work is also partially supported by the funding plan for scientific research activities of academic and technical leaders and reserve candidates in Anhui Province (Grant No. 2021H264) and the university Natural Science Research Project of Anhui Province (Grant No. KJ2019A0593, KJ2019A0952).

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Authors and Affiliations

  1. School of Physics and Electronic Information, Huaibei Normal University, Huaibei, 235000, China

    Yu Deng, Feng Zou & Yuan Chen

  2. School of Computer Science and Technology, Huaibei Normal University, Huaibei, 235000, China

    Debao Chen, Ying Zheng, Minglan Fu & Chun Wang

Authors
  1. Yu Deng
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  2. Debao Chen
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  3. Feng Zou
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  4. Yuan Chen
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  5. Ying Zheng
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  6. Minglan Fu
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  7. Chun Wang
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Contributions

Conceptualization: [Debao Chen]; Methodology: [Yu Deng]; Software: [Yu Deng], [Yuan Chen]; Writing - original draft preparation: [Yu Deng]; Writing - review and editing: [Debao Chen], [Feng Zou]; Funding acquisition: [Debao Chen], [Ying Zheng]; Visualization: [Feng Zou]; Validation: [Ying Zheng], [Minglan Fu]; Investigation: [Chun Wang];

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Correspondence to Debao Chen.

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Deng, Y., Chen, D., Zou, F. et al. Heterogeneous ensemble algorithms for function optimization. Appl Intell 52, 13310–13338 (2022). https://doi.org/10.1007/s10489-022-03197-w

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