A multi-sample particle swarm optimization algorithm based on electric field force

Shangbo Zhou; Yuxiao Han; Long Sha; Shufang Zhu; Shangbo Zhou; Yuxiao Han; Long Sha; Shufang Zhu

doi:10.3934/mbe.2021369

Mathematical Biosciences and Engineering

2021, Volume 18, Issue 6: 7464-7489. doi: 10.3934/mbe.2021369

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Research article

A multi-sample particle swarm optimization algorithm based on electric field force

1.
College of Computer Science, Chongqing University, Chongqing 400044, China
2.
Key Laboratory of Dependable Service Computing in Cyber Physical Society, Ministry of Education, Chongqing 400030, China

Received: 10 June 2021 Accepted: 23 August 2021 Published: 31 August 2021

Aiming at the premature convergence problem of particle swarm optimization algorithm, a multi-sample particle swarm optimization (MSPSO) algorithm based on electric field force is proposed. Firstly, we introduce the concept of the electric field into the particle swarm optimization algorithm. The particles are affected by the electric field force, which makes the particles exhibit diverse behaviors. Secondly, MSPSO constructs multiple samples through two new strategies to guide particle learning. An electric field force-based comprehensive learning strategy (EFCLS) is proposed to build attractive samples and repulsive samples, thus improving search efficiency. To further enhance the convergence accuracy of the algorithm, a segment-based weighted learning strategy (SWLS) is employed to construct a global learning sample so that the particles learn more comprehensive information. In addition, the parameters of the model are adjusted adaptively to adapt to the population status in different periods. We have verified the effectiveness of these newly proposed strategies through experiments. Sixteen benchmark functions and eight well-known particle swarm optimization algorithm variants are employed to prove the superiority of MSPSO. The comparison results show that MSPSO has better performance in terms of accuracy, especially for high-dimensional spaces, while maintaining a faster convergence rate. Besides, a real-world problem also verified that MSPSO has practical application value.
- particle swarm optimization,
- electric field force,
- comprehensive learning,
- segmented learning,
- parameter adaptation
Citation: Shangbo Zhou, Yuxiao Han, Long Sha, Shufang Zhu. A multi-sample particle swarm optimization algorithm based on electric field force[J]. Mathematical Biosciences and Engineering, 2021, 18(6): 7464-7489. doi: 10.3934/mbe.2021369

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Abstract

Aiming at the premature convergence problem of particle swarm optimization algorithm, a multi-sample particle swarm optimization (MSPSO) algorithm based on electric field force is proposed. Firstly, we introduce the concept of the electric field into the particle swarm optimization algorithm. The particles are affected by the electric field force, which makes the particles exhibit diverse behaviors. Secondly, MSPSO constructs multiple samples through two new strategies to guide particle learning. An electric field force-based comprehensive learning strategy (EFCLS) is proposed to build attractive samples and repulsive samples, thus improving search efficiency. To further enhance the convergence accuracy of the algorithm, a segment-based weighted learning strategy (SWLS) is employed to construct a global learning sample so that the particles learn more comprehensive information. In addition, the parameters of the model are adjusted adaptively to adapt to the population status in different periods. We have verified the effectiveness of these newly proposed strategies through experiments. Sixteen benchmark functions and eight well-known particle swarm optimization algorithm variants are employed to prove the superiority of MSPSO. The comparison results show that MSPSO has better performance in terms of accuracy, especially for high-dimensional spaces, while maintaining a faster convergence rate. Besides, a real-world problem also verified that MSPSO has practical application value.

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