Population evaluation of the adapted particle swarm optimization algorithm applied for control in view of unknown parameter changes in the system

Karmouche, Diogo Cunha José; Chavarette, Fábio Roberto; Abreu, Gustavo; Gonçalves, Aparecido Carlos

doi:10.1007/s40430-022-04002-y

Cited by 3 publications

(1 citation statement)

References 21 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…1) Parameter adjustment is a critical aspect. Inertial weight is expressed in diverse forms, encompassing linearly decreasing inertial weight [5], fuzzy adaptive inertial weight [6], inertial weight [7], and chaotic dynamic weight [8,9]. This parameter has garnered considerable interest owing to its capacity to balance population exploration and exploitation.…”

Section: Introductionmentioning

confidence: 99%

An effective particle swarm optimization algorithm with disturbance

2024

Third International Conference on Electronic Information Engineering, Big Data, and Computer Technology (EIBDCT 2024)

View full text Add to dashboard Cite

Particle swarm optimization (PSO) has been widely studied because of its advantages of fast convergence and few control parameters, therefore many improvements have been proposed. However, some variants of PSO inevitably increase the parameters needed to tune and computation cost and lose the advantages of faster convergence and easier implementation of the PSO algorithm. To overcome these problems, this paper proposes an improved PSO, which is called EPSO. Compared with the standard particle swarm optimization algorithm, the acceleration coefficient is removed and the learning factor is replaced to reduce the algorithm's dependency on its parameters. Noise-based disturbance is introduced to escape local optima. Afterward, a large number of benchmark functions were used to detect the performance of EPSO, and experimental results showed that it had higher accuracy and faster convergence speed.

show abstract