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

Dynamic interval multi-objective optimization problems, such as those encountered in wireless sensor network scheduling and portfolio selection, are increasingly prevalent. However, they present ...

This paper makes the following key contributions: 1. An evolutionary algorithm based on a neo-cooperation search is proposed, known as NCSEA, which allocates computational resources to two ...

Therefore, this paper proposed a two-objective problem model for the channel selection problem and introduced a domain knowledge-assisted multi-objective optimization algorithm (DK-MOEA) to solve the ...

Multi-objective evolutionary optimization is widely utilized in industrial design. Despite the success of multi-objective evolutionary optimization algorithms in addressing complex optimization ...

In this paper, we pose the goal programming problem as a multi-objective optimization problem of minimizing deviations from individual goals and then suggest an evolutionary optimization algorithm to ...

However, using evolutionary algorithms to address multi-objective optimization problems requires a large number of real evaluations, which often consumes significant time, manpower, and financial ...