Ant algorithms for discrete optimization
Artificial Life
Future Generation Computer Systems
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Journal of Global Optimization
A parallel implementation of ant colony optimization
Journal of Parallel and Distributed Computing - Problems in parallel and distributed computing: Solutions based on evolutionary paradigms
An Electromagnetism-like Mechanism for Global Optimization
Journal of Global Optimization
Parallelization Strategies for Ant Colony Optimization
PPSN V Proceedings of the 5th International Conference on Parallel Problem Solving from Nature
Metaheuristics in combinatorial optimization: Overview and conceptual comparison
ACM Computing Surveys (CSUR)
A comparative study of stochastic optimization methods in electric motor design
Applied Intelligence
An artificial intelligence approach to the efficiency improvement of a universal motor
Engineering Applications of Artificial Intelligence
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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. A new, distributed version of the multilevel ant-stigmergy algorithm (MASA) was proposed to solve this optimization problem. The roots of the MASA can be found in the ant-colony optimization metaheuristic. The usefulness and efficiency of some sequential population-based algorithms (electromagnetism-like, particle swarm, evolutionary, differential evolution) as well as a sequential version of the MASA for solving the problem of minimizing the losses in an electric motor have recently been reported in the literature. Results showed that the MASA outperformed all the other algorithms. Due to a time-consuming solution evaluation, however, all the sequential algorithms were inefficient in terms of time. In order to remedy this shortcoming, 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 (from one day to a few hours) without any noticeable reduction in the quality of the solution.