As the complexity of modern electronic systems increases, optimization strategies that simultaneously balance multiple, often conflicting objectives become more pressing. Part 3 of a three-part series on optimization, this paper introduces the transition from single-objective to multi-objective optimization, highlights leading algorithmic approaches, and details the role of MO-SHERPA, an adaptive strategy embedded in Siemens’ optimization toolset. We examine how these methods effectively explore complex design spaces and provide data-driven insights into performance trade-offs within the context of PCB optimization to support more robust, informed decision-making.
Shivani Joshi, Siemens EDA