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