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Framework for wrapping binary swarm optimizers to the hybrid parallel cooperative coevolving version

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Abstract

In recent decades with the increase in the complexity of the problems, the need for high-performance and scalable optimization tools has been inevitable. Among different phenomena introduced to optimization problems, naturally inspired algorithms are favored. Also, encountering large-scale problems, high-performance tools like parallel implementations should be needed. In order to tackle this problem, the framework has been proposed that can wrap any swarm algorithm into an outperformer parallel and hybrid version. Six accepted swarm algorithms are selected to evaluate performance and compare the wrapped version with standard versions. Six nonlinear high-dimension benchmark functions are used to test the proposed algorithms. The experimental results show that wrapped versions outperform standard versions with the measurement of average best fitness.

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Authors and Affiliations

  1. Faculty of Electrical and Computer Engineering, University of Tabriz, Tabriz, Iran

    Mohammadreza Ipchi Sheshgelani, Saeid Pashazadeh & Pedram Salehpoor

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  1. Mohammadreza Ipchi Sheshgelani
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  2. Saeid Pashazadeh
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  3. Pedram Salehpoor
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Contributions

MIS: Conceptualization, Methodology, Implementing, Review, and Editing. SP: Supervision, Validating, Reviewed, and Edited. PS: Reviewing.

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Correspondence to Saeid Pashazadeh.

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Ipchi Sheshgelani, M., Pashazadeh, S. & Salehpoor, P. Framework for wrapping binary swarm optimizers to the hybrid parallel cooperative coevolving version. Cluster Comput 27, 1683–1697 (2024). https://doi.org/10.1007/s10586-023-04029-3

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