This article presents an optimization method used at the electric-motor design. The goal of the optimization was to find the geometrical parameter values that would generate the rotor and the stator geometries with minimum power losses. Due to a time-consuming solution evaluation the sequential algorithm is inefficient in terms of time. For this reason, a new, efficient distributed implementation of the MASA is presented. In addition, we have shown that with distributed computing the computation time can be drastically reduced without any noticeable reduction in the quality of the solution.
COBISS.SI-ID: 21585191
In this paper we propose a promising optimization algorithm, referred to as the Multilevel Ant Stigmergy Algorithm (MASA), which exploits stigmergy in order to optimize multi-parameter functions. We evaluate the performance of the MASA and Differential Evolution - one of the leading stochastic method for numerical optimization - in terms of their applicability as numerical optimization techniques. The comparison is performed using several widely used benchmark functions with added noise.
COBISS.SI-ID: 21826087