Multi-Objective Constrained Optimization for Energy Applications via Tree Ensembles

  title={Multi-Objective Constrained Optimization for Energy Applications via Tree Ensembles},
  author={Alexander Thebelt and Calvin Tsay and Robert M. Lee and Nathan Sudermann-Merx and D. Walz and Thomas G. Tranter and Ruth Misener},

Deep Surrogate of Modular Multi Pump using Active Learning

This work develops an active learning framework for estimating the operating point of a Modular Multi Pump used in energy and applies Active learning to estimate the surge distance with minimal dataset.

Bio-high entropy alloys: Progress, challenges, and opportunities

The composition system of Bio-HEAs in recent years is summarized, their biocompatibility and mechanical properties of human bone adaptation are introduced, and the following suggestions for the development direction are put forward.



Multi-Objective Wind Farm Layout Optimization Considering Energy Generation and Noise Propagation With NSGA-II

Recently, the environmental impact of wind farms has been receiving increasing attention. As land is more extensively exploited for onshore wind farms, they are more likely to be in proximity with

MVMOO: Mixed variable multi-objective optimisation

The MVMOO algorithm is proposed, a new multi-objective algorithm capable of optimising both continuous and discrete bounded variables in an efficient manner that utilises Gaussian processes as surrogates in combination with a novel distance metric based upon Gower similarity.

ParEGO: a hybrid algorithm with on-line landscape approximation for expensive multiobjective optimization problems

Results show that NSGA-II, a popular multiobjective evolutionary algorithm, performs well compared with random search, even within the restricted number of evaluations used.

Towards a real-time Energy Management System for a Microgrid using a multi-objective genetic algorithm

This paper proposes a real-time Energy Management System (EMS) for a low voltage (LV) Microgrid (MG). The system operation consists in solving the Unit Commitment (UC) and Economic Load Dispatch

Efficient multiobjective optimization employing Gaussian processes, spectral sampling and a genetic algorithm

A new algorithm is proposed, TSEMO, which uses Gaussian processes as surrogates, which gives a simple algorithm without the requirement of a priori knowledge, reduced hypervolume calculations to approach linear scaling with respect to the number of objectives, the capacity to handle noise and the ability for batch-sequential usage.