Ant algorithms for discrete optimization
Artificial Life
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
An Electromagnetism-like Mechanism for Global Optimization
Journal of Global Optimization
Hi-index | 0.01 |
This paper presents a comparative study of two stochastic optimization methods: the electromagnetism-like algorithm (EMA) and the multilevel ant stigmergy algorithm (MASA) in computer-assisted design of universal electric motor rotor/stator geometry. The design goal was to minimize the power losses. The output of this study can be summarized in several important findings. Above all, both compared optimization methods were able to significantly improve the original engineering design. Comparing the tested methods, the MASA generated the minimum power loss designs. Its additional advantage shown on this problem was the capability of successfully performing the optimization from random starting points, which was not the case with the EMA.