Multi-Objective Optimization (MOO) is a mathematical approach to solving problems with multiple conflicting objectives by finding optimal trade-offs. Instead of a single best solution, MOO identifies a set of Pareto-optimal solutions where improving one objective may compromise another. It is widely used in engineering, finance, energy management, and artificial intelligence to balance efficiency, cost, performance, and sustainability in complex decision-making scenarios.
Designing drug molecules is no longer just about generation, but control. NEC Laboratories America introduces MolDiffdAE, a diffusion-based framework that enables precise, multi-objective tuning of 3D molecular properties. By learning a semantic space, researchers can efficiently guide design, accelerating drug discovery and exploration of chemical space.
This paper presents a method to determine optimal energy and power capacity of distributed Energy Storage Systems (ESS) in behind-the-meter applications to maximize local Photovoltaic (PV) utilization or minimize Demand Charge (DC) cost. The problem is solved as a multi-objective optimization model to obtain a set of Pareto optimal solutions for each scenario in each month. An approach is then presented to map the monthly Pareto fronts into a single yearly Pareto front. A cost benefit analysis has also been carried out to show the compromise between PV utilization, DC cost, and ESS cost.
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NEC Laboratories America, Inc. (NEC Labs) is the US-based center for NEC Corporation’s global network of corporate research laboratories. Our diverse research groups collaborate with industry, academia and governments to provide disruptive solutions to complex problems. A leader in the integration of IT and network technologies with more than 100 years of expertise, NEC provides a combination of products and solutions that cross-utilize the company’s experience and global resources to meet the complex and ever-changing needs of its customers.