Multi-objective optimisation using evolutionary algorithms constitutes a powerful computational framework that addresses complex problems involving conflicting objectives.

Second, the conversion to a single-objective optimization problem involves additional constraints. Third, since most real-world goal programming problems involve nonlinear criterion functions, the ...

Alireza Sahebgharani, MULTI-OBJECTIVE LAND USE OPTIMIZATION THROUGH PARALLEL PARTICLE SWARM ALGORITHM, Journal of Urban and Environmental Engineering, Vol. 10, No. 1 (January to June 2016), pp. 42-49 ...

Icing on wind turbine blades reduces energy & can cause damage. New algorithms improve rapid, accurate detection using machine learning. A model, MACOA-IWKELM, boosts performance with weighted data & ...

Computational optics integrates optical hardware and algorithms, enhancing imaging capabilities through joint optimization ...

Machine learning algorithms are gaining popularity in the hydrologic sciences. These algorithms often require tuning hyperparameters to tailor their performance to a specific purpose. Often these ...

MicroAlgo Inc. (the "Company" or "MicroAlgo") (NASDAQ: MLGO), today announced the development of an innovative hybrid algorithm that combines the advantages of classical and quantum computing to ...