On the one hand, to tailor EAs for solving LSMOPs, the related works are highly related to multi‐objective evolutionary algorithms (MOEAs). While conventional MOEAs were developed to solve problems ...

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

Numerous Multi-objective Evolutionary Algorithms (MOEAs) and nature-inspired metaheuristic algorithms have been created during the last three decades to handle real-world MOO issues. In ( Zitzler and ...

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 ...

One of the primary challenges in wind energy development is optimizing the spatial layout of wind turbines within a given site. Poor turbine placement can lead to reduced energy capture due to wake ...

As cities worldwide begin embracing low-altitude logistics to support rapid, flexible deliveries by drones, urban planners face an increasingly ...

Summary . In addition to developing multi‐objective evolutionary algorithms (MOEAs) for solving general large scale multi‐objective optimization problems (LSMOPs), it is reasonable to improve the ...