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ASPSO: an adaptive acceleration coefficient particle swarm optimization algorithm with sigmoid inertia weight
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DOI：10.1007/s10586-025-05409-7.png)
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Abstract
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Although the particle swarm optimization (PSO) algorithm has achieved better results in many optimization problems, it is easy to fall into local optimum and premature convergence when facing high-dimensional optimization problems. Therefore, a new particle swarm optimization algorithm, the Adaptive Acceleration Coefficients Particle Swarm Optimization Algorithm with Sigmoid Inertia Weight (ASPSO), is proposed. The algorithm contains three effective improvements: firstly, an innovative Sigmoid inertia weight is proposed, and the effectiveness of the new inertia weight in optimization performance is verified by comparing it with several known inertia weights. Secondly, a new indicator “Individual Approximation Coefficients” (IAC) is introduced to quantify the particle’s approximation relative to the global optimum, and an adaptive acceleration coefficients strategy is designed based on the IAC. Finally, the velocity update formula is improved based on the position information of the particle in the previous iteration. The new formula enhances the utilization of the later more optimal position. To evaluate the performance of the improved algorithm, a simulation experiment was designed to compare the ASPSO algorithm with six other well-known algorithms. The simulation experiment shows that the ASPSO algorithm outperforms the other well-known algorithms in terms of global search capability and overall performance. In addition, applying the ASPSO algorithm to a specific engineering example yielded the best results to date.
Keywords:
Particle swarm optimization
Adaptive acceleration coefficients
Sigmoid inertia weight
Local optimum
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