College of Computer Science, Chongqing University, Chongqing 400044, China.
Key Laboratory of Dependable Service Computing in Cyber Physical Society, Ministry of Education, Chongqing 400030, China.
Math Biosci Eng. 2021 Aug 31;18(6):7464-7489. doi: 10.3934/mbe.2021369.
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.
针对粒子群优化算法的过早收敛问题，提出了一种基于电场力的多样本粒子群优化（MSPSO）算法。首先，将电场的概念引入到粒子群优化算法中，粒子受到电场力的影响，使其表现出多样化的行为。其次，MSPSO 通过两种新策略构建多个样本，指导粒子学习。提出了基于电场力的综合学习策略（EFCLS）来构建吸引样本和排斥样本，从而提高搜索效率。为了进一步提高算法的收敛精度，采用基于分段的加权学习策略（SWLS）构建全局学习样本，使粒子学习更全面的信息。此外，自适应调整模型参数以适应不同阶段的种群状态。通过实验验证了这些新策略的有效性。使用十六个基准函数和八个著名的粒子群优化算法变体来证明 MSPSO 的优越性。比较结果表明，MSPSO 在准确性方面表现更好，特别是在高维空间，同时保持更快的收敛速度。此外，一个实际问题也验证了 MSPSO 具有实际应用价值。
