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Proceedings of CAD'14, 2014, 175-177
Multi-objective Topology Optimization with Ant Colony Optimization and Genetic Algorithms

João Batista Zuliani, Centro Federal de Educação Tecnológica de Minas
Miri Weiss Cohen, ORT Braude College of Engineering, Karmiel
Lucas de Souza Batista, Frederico Gadelha Guimarães, Universidade Federal de Minas Gerais

Abstract. Topology Optimization (TO) is a design process used to explore new designs by optimally distributing material in the design region. The best designs are achieved in accordance with the objectives and constraints defined for a specific application. Since the pioneering works by Bendsoe and Kikuchi, TO methods have been applied to various physical systems, including electromagnetic devices and machines. In this study, we present a multi-objective approach for TO that uses multi-objective evolutionary algorithms. The first stage consists of applying a multi-objective Ant Colony Optimization (ACO) to find tradeoff topologies with different material distributions. In the second stage, we parameterize the boundaries of the topologies found by using NURBS. Multi-objective genetic algorithms are applied as a heuristic optimization engine to optimize the control points of the curves in order to smooth and refine the boundaries of the topology. The main advantage of this multi-objective approach is that the designer can identify, explore and refine a number of tradeoff topologies. The proposed methodology is illustrated in the design of an Interior Permanent Magnet (IPM) machine.

Keywords. Topology optimization, ant colony optimization, genetic algorithms, NURBS, machine design

DOI: 10.14733/cadconfP.2014.175-177