ParMOO: A Python library for parallel multiobjective simulation optimization

Abstract

A multiobjective optimization problem (MOOP) is an optimization problem in which multiple objectives are optimized simultaneously. The goal of a MOOP is to find solutions that describe the tradeoff between these (potentially conflicting) objectives. Such a tradeoff surface is called the Pareto front. Real-world MOOPs may also involve constraints – additional hard rules that every solution must adhere to. In a multiobjective simulation optimization problem, the objectives are derived from the outputs of one or more computationally expensive simulations. Such problems are ubiquitous in science and engineering. ParMOO is a Python framework and library of solver components for building and deploying highly customized multiobjective simulation optimization solvers. ParMOO is designed to help engineers, practitioners, and optimization experts exploit available structures in how simulation outputs are used to formulate the objectives for a MOOP.