Approximate multi-objective optimization using conservative and feasible moving least squares method: application to automotive knuckle design

The original version of the moving least squares method (MLSM) does not always ensure solution feasibility for nonlinear and/or non‐convex functions in the context of meta‐model‐based approximate optimization. The paper explores a new implementation of MLSM that ensures the conservative feasibility of Pareto optimal solutions in non‐dominated sorting genetic algorithm (NSGA‐II)‐based approximate multi‐objective optimization. We devised a ‘conservative and feasible MLSM’ (CF‐MLSM) to realize the conservativeness and feasibility of multi‐objective Pareto optimal solutions for both unconstrained and constrained problems. We verified the usefulness of our proposed approach by exploring strength‐based sizing optimization of an automotive knuckle component under bump and brake loading constraints.