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

This paper frames hardware-aware neural network pruning as a multi-objective optimization problem and introduces HAMP, a memetic Multi-Objective Evolutionary Algorithm (MOEA) that optimizes both ...

Other algorithms, such as the Multi-Objective Evolutionary Algorithm based on Decomposition (MOEA/D), break the optimization problem into smaller sub-problems, each representing a weighted combination ...

This article explores the use of genetic algorithms to optimize the operation and maintenance assets of an offshore wind farm. Three different methods are implemented in order to demonstrate the ...

Multi-Objective Genetic Algorithms determine the Pareto set by giving higher priority to dominant portfolios in the evolutionary optimization techniques of selection and reproduction.

In an era where autonomous systems demand pinpoint accuracy, navigation algorithms face a tough trade-off between precision and speed.

Mathematical perfection in optimization is only as justified as the OF calculation is complete and true. The user needs to choose the optimizer, and the application characteristics should drive the ...

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 ...