(untitled) (REVUE ROUMAINE DES SCIENCES TECHNIQUES — SÉRIE ÉLECTROTECHNIQUE ET ÉNERGÉTIQUE, 2025)
The spiral dynamic algorithm (SDA) is a metaheuristic characterized by the setting of parameters (spiral radius and rotational angle). The drawback of all meta-heuristic methods is the premature convergence, which occurs when a trade-off between exploitation and exploration is not maintained. SDA provides a good exploitation phase because all points are attracted to the best solution. But the exploration phase is poor when the spiral parameters are set to constant values during the whole search process. To improve SDA performance and circumvent premature convergence, this paper proposes an enhanced SDA in which the parameter settings vary simultaneously according to nonlinear functions. The effectiveness of the enhanced SDA algorithm (ESDA) was proven by identifying the electrical and mechanical induction motor (IM) parameters. This is achieved using the reference model method, in which the estimated parameters correspond to the minimum of the objective function. A comparison is established between the ESDA, SDAs, genetic algorithm (GA), and particle swarm optimization (PSO). The developed program and the estimation approach are tested using simulated and measured data from an IM (1.5 kW).