globalMOO represents a paradigm shift in multi-objective optimization and inverse solution of complex problems
globalMOO is a breakthrough technology for multi-objective optimization and the efficient solution of inverse problems. We explain why globalMOO is needed, what it is, and where it is applicable.
The precisely calculated knowledge capture in globalMOO leads to understanding of the cause-and-effect relationships inherent in the forward model. globalMOO uses that knowledge to decide on how to take the next step in the inverse solution process.
globalMOO excels in aerospace, manufacturing, healthcare, and supply chain management, offering a solution for complex multi-objective optimization challenges with superior data efficiency and performance.
At the heart of globalMOO is a proprietary algorithm designed to ensure a minimal number of model evaluations. The tool requires far fewer evaluations of external systems than competing methods, thereby providing quicker and more reliable outcomes. This is true even for large numbers of variables and objectives.
Understanding the need for seamless integration, globalMOO offers a variety of API interfaces, including a Python Software Development Kit (SDK) that wraps around the proprietary Dynamic-Link Library (DLL) engine. For those seeking web-based implementations, a robust Web API is available, ensuring a multitude of avenues for system interaction.
Scalability is a primary concern in optimization. globalMOO is designed to scale seamlessly, handling hundreds of input variables and hundreds of outcomes. While the software supports basic multi-core parallelism, its efficient architecture and native speed negates the need for extensive parallel computing, making it a resource-friendly choice for complex operations.
While the challenge of providing absolute guarantees in optimization exists, especially for black-box models, globalMOO assures global solutions as long as the sampling of the search space is adequate. This becomes a fundamental assurance in scenarios that require global optimization for operational success.
globalMOO is designed to seamlessly integrate with AI-driven systems. It uncovers and quantifies the biases associated with each input variable through its proprietary bias calculation methodology. This is essential for maintaining responsible and reliable AI operations.
globalMOO shifts multi-objective optimization, offering a proprietary algorithm with scalability and bias mitigation for industry-wide versatility. It enhances manufacturing, medical, and logistical efficiencies with superior computational flexibility.
We compared globalMOO capabilities with other available solutions. In every test globalMOO beats the competitor in convergence speed and efficiency. The globalMOO efficiency improves as the number of input variable and/or objectives increase. Below is a comparison of globalMOO solution efficiency with MOED, DNSGA2 and NSGA2 algorithms for different number of input variables.
In this example, up to 32 input variables with various output parameters were compared. The scale of the plot is logarithmic and and each dashed line represents an order of magnitude increase in iterations (or run time). For various sample problems MOEAD, DNSGA2 need up to 100,000 iterations to solve a 30 variable problem. globalMOO requires less than 100 iterations for optimization, using up to 1000 training cases. This means, once the training is complete, it can be used for achieving other targets and objectives with less than 100 iterations. That’s 1000 times fewer iterations than over available solutions.