We've just launched globalMOO, a novel agent-driven, multi-objective optimization API designed for complex inverse problem-solving and optimization scenarios.<p>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.<p>Key highlights:<p>True Multi-Objective Optimization: No scalarization or subjective weighting needed. Directly solves problems with objectives in differing units and scales, simultaneously.<p>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.<p>Data-Efficient: Significantly fewer model evaluations compared to standard methods (MOEAD, DNSGA2, NSGA2), often requiring orders of magnitude fewer iterations.<p>High Scalability: Easily scales to 200+ input variables and hundreds of objectives.<p>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.<p>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.<p>Register for a free trial of the web API at <a href="https://app.globalmoo.com/" rel="nofollow">https://app.globalmoo.com/</a> or check out a variety of example implementations to get you up and running at <a href="https://github.com/globalMOO">https://github.com/globalMOO</a>.