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

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 ...

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The team's findings further revealed that, while multi-objective evolutionary algorithms hold significant potential, they still struggle with low search efficiency.

While the first variant, MOBO1, depends on the sort and grid-index approach, the second variant, MOBO2, relies on the Non-dominated Sorting Genetic Algorithm-II (NSGA-II) algorithm technique. The last ...

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 ...