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

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

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 ...

Xiaoseng Zhang, Multi-objective Optimization Design in Construction Period Considering the Influence of Marine Climate, Journal of Coastal Research, SPECIAL ISSUE NO. 115. Advances in Water Resources, ...

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 ...

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 ...

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