Auto-Adaptive Search Capabilities of the New Borg MOEA: A Detailed Comparison on Alternative Product Family Design Problem Formulations

Abstract

There is a growing recognition that the design and management of complex engineered systems requires a fundamental advance in our ability to identify and exploit their inherent tradeoffs across a growing number of decisions and objectives. In support of this challenge, this study provides a rigorous evaluation of modern “many-objective” evolutionary optimization algorithms. The computational power of modern high-performance computing environments makes it possible to investigate optimization algorithm performance in ways that were not historically feasible. This study uses millions of algorithm runs, each performing hundreds of thousands of function evaluations, to do a Sobol’ global sensitivity analysis on algorithm parameterization. We present this analysis for two algorithms across four formulations of a General Aviation Aircraft (GAA) conceptual product family design problem. The two algorithms are the recently introduced Borg Multi-Objective Evolutionary Algorithm (MOEA), a promising auto-adaptive multi-operator search algorithm, and the e-MOEA, its algorithmic forebear. The four formulations of the GAA problem vary in their complexity and allow us to investigate the assumption that complex problem formulations are more difficult to solve.