.png){kind=icon}
.png){kind=icon}
Return
Particle swarm optimization algorithm using complex-order derivative concept: A comprehensive study
.png){kind=icon}
PRE
AI
DOI：10.1016/j.asoc.2021.107641.png)
.png){kind=icon}
Original request for help.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
This paper presents a comprehensive study of the Particle Swarm Optimization (PSO) algorithm, called complex-order PSO (CPSO). In the core of new set of algorithms, we employ the complex-order derivative and the conjugate order differential concepts in the position and velocity adaption mechanisms. To determine the influence of the control parameters on the quality of the results, a sensitivity analysis is conducted. A number of value- and rank-based tests assesses the algorithms' performance. For a suite of benchmark functions, the standard deviation and the mean best of the results are reported. Additionally, the Friedman test specifies the average ranking from the obtained results. The effect of the complex-order operation and the population size are analyzed using the Taguchi test. An application example illustrates the performance of the CPSO. (C) 2021 Elsevier B.V. All rights reserved.
Keywords:
Fractional calculus
Complex-order
Particle swarm optimization
Sensitivity analysis
AI Summary
Key information extracted from the uploaded paper, including a brief overview, abstract, background, key highlights, visual analysis, and future outlook.
Journal
IF:
6.6
Papers:
1.4W
Citations:
4.8W
Organization
View more organizations