Constrained design optimization of active magnetic bearings through an artificial immune system

Abstract

Purpose The purpose of this paper is to present the use of artificial immune systems (AISs) to solve constrained design optimization problems for active magnetic bearings (AMBs). Design/methodology/approach This research applies the AIS approach, more specifically, a representative clonal selection-based AIS called CLONALG, to the single-objective structural design optimization of AMBs. In addition, when compared with a genetic algorithm (GA) developed in the previous work, the CLONALG fails to produce best solutions when a nearly zero feasible ratio occurs in an AMB design problem. Therefore, an AIS called ARISCO (AIS for constrained optimization) is proposed to address the above issue. Findings A total of six AMB design cases are solved by the GA, CLONALG, and ARISCO. Based on the simulation results, in terms of solution quality, the ARISCO is shown to have better overall performance than the CLONALG and GA. In particular, when solving a problem with a nearly zero feasible ratio, the ARISCO and GA perform equally and both outperform the CLONALG. Originality/value In summary, the contributions of this paper include: this research applies the AIS approach, more precisely, the CLONALG, to the single-objective structural design optimization of AMBs; the ARISCO overall produces better AMB designs than the CLONALG and a GA developed in the previous work; in situations where a nearly zero feasible ratio occurs, the ARISCO and GA perform equally, and they both outperform the CLONALG.

Univariate Financial Time Series Prediction using Clonal Selection Algorithm

“…In this study, an evolutionary algorithm inspired by the clonal selection theory called CSA is proposed to solve the problem of univariate financial time series prediction. This algorithm is chosen because based on a survey by Luo and Lin [20], CSA has been applied to many optimization problems, including constrained optimization [21], combinatorial optimization [22], and nonlinear optimization [23]. Furthermore, according to Hu, Sun, Nie, Li, and Liu [24], CSA is less complicated than GA because it does not have crossover operation which reduces the computational cost.…”