Industrial Portfolio Management for Many-Objective Optimization Algorithms

In industry we see an increasing interest in (evolutionary) many objective optimization algorithms. However, the majority of engineers only using, not researching, optimizers have a limited understanding of the pros and cons of different algorithms and therefore rely on either third-party recommendations or benchmark tests to pick the most suitable methods for their problems. Unfortunately, most benchmarks are targeting an academic audience leaving the practitioner often in doubt about the correct choices. In this article we try to outline the essential requirements for a many-objective optimization algorithm portfolio management from an industrial perspective and compare the situation in our field to another domain with similar issues, image processing. We want to address one of the core practical issues: “Given a limited computational or time budget for my optimization project, which optimization algorithms should I try?”.