Multi-objective Topology Optimization with Ant Colony Optimization and Genetic Algorithms

Abstract

In this work, we present a multi-objective approach for Topology Optimization applied to the design of devices with several materials. The first stage consists of applying a Multi-Objective Ant Colony Optimization (MOACO) to find tradeoff topologies with different material distributions. In the second stage, we parameterize the boundaries of the topologies found by using NURBS. A Multi-objective Genetic Algorithm is applied as a heuristic optimization engine to optimize the control points, weights and knots of the curves in order to smooth and refine the boundaries of the topology. The main advantage of a 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 a C-core magnetic actuator.