Autonomous Pareto Front Scanning using an Adaptive Multi-Agent System for Multidisciplinary Optimization

Abstract

Multidisciplinary Design Optimization (MDO) problems can have a unique objective or be multi-objective. In this paper, we are interested in MDO problems having at least two conflicting objectives. This characteristic ensures the existence of a set of compromise solutions called Pareto front. We treat those MDO problems like Multi-Objective Optimization (MOO) problems. Actual MOO methods suffer from certain limitations, especially the necessity for their users to adjust various parameters. These adjustments can be challenging, requiering both disciplinary and optimization knowledge. We propose the use of the Adaptive Multi-Agent Systems technology in order to automatize the Pareto front obtention. ParetOMAS (Pareto Optimization Multi-Agent System) is designed to scan Pareto fronts efficiently, autonomously or interactively. Evaluations on several academic and industrial test cases are provided to validate our approach.