We've just launched globalMOO, a novel agent-driven, multi-objective optimization API designed for complex inverse problem-solving and optimization scenarios.

Unlike traditional methods that rely heavily on scalarization, heuristic tuning, or exhaustive searches, globalMOO leverages a proprietary agent-based system to achieve highly efficient optimization across diverse and conflicting objectives - converging on optima 1000x faster than traditional methods in benchmarked high-dimensional use cases.

Key highlights:

True Multi-Objective Optimization: No scalarization or subjective weighting needed. Directly solves problems with objectives in differing units and scales, simultaneously.

Inverse Solution Capability: Model-agnostic and adept at optimizing black-box, physics-based, or AI-driven models. No need to expose or to modify the contents of your algorithms.

Data-Efficient: Significantly fewer model evaluations compared to standard methods (MOEAD, DNSGA2, NSGA2), often requiring orders of magnitude fewer iterations.

High Scalability: Easily scales to 200+ input variables and hundreds of objectives.

Versatile Integration: Provides robust SDKs in Python, C#, PHP and JavaScript for interacting with the Web API, and various local DLL/.so interfaces for local installation, facilitating seamless integration into existing workflows.

We've applied globalMOO in diverse fields like petroleum engineering, manufacturing, and shipping, consistently outperforming existing algorithms in terms of computational efficiency and convergence speed.

Register for a free trial of the web API at https://app.globalmoo.com/ or check out a variety of example implementations to get you up and running at https://github.com/globalMOO.