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

Real-world optimization problems often have multi-ple conflicting objective functions to be optimized simultaneously. In some of them, there are different Pareto optimal solutions with the same ...

Recently, transfer learning has received more and more attention in the field of computational intelligence. The multi-task paradigm is a recent research hotspot. Among them, multi-objective ...

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

The experimental results show that NCSEA outperforms the compared constrained multi-objective evolutionary algorithms in most test instances and demonstrates stable performance across test problems of ...

This paper presents a two-stage sparse multi-objective evolutionary algorithm (TS-MOEA) to address channel selection problems in brain-computer interface systems. Methods: In TS-MOEA, a two-stage ...

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 ...