Harnessing multi-objective simulated annealing toward configuration optimization within compact space for additive manufacturing

The rapid advancement of additive manufacturing technology has led to new opportunities and challenges. One potential advantage of additive manufacturing is the possibility of producing systems with reduced volumes/ weights. This research concerns a type of configuration optimization problems, where the envelope volume in space occupied by a number of components is to be minimized along with other objectives. Since in practical applications the objectives and constraints are usually complex, the formulation of computationally tractable optimization becomes difficult. Moreover, unlike conventional multi-objective problems, these configuration optimization problems usually come with a number of demanding constraints that are hard to satisfy, which results in the critical challenge of balancing solution feasibility with optimality. In this research, the mathematical formulation of a representative problem of configuration optimization with multiple hard constraints is presented first, followed by two newly developed versions of an enhanced multi-objective simulated annealing approach, referred to as MOSA/R, to solve this challenging problem. To facilitate the optimization computationally, in MOSA/R, a versatile re-seed scheme allowing biased search while avoiding pre-mature convergence is designed. Re-seed can generally lead to more comprehensive search in the parametric space. Case studies indicate that the new algorithm yields significantly improved performance towards both constrained benchmark tests and constrained configuration optimization problem. The methodology developed can lead to an integrated framework of design and additive manufacturing.