Realistic problems typically have many conflicting objectives. Therefore, it is instinctive to
look at the engineering problems as multi-objective optimization problems. This paper briefly …

The development and application of evolutionary algorithms (EAs) and other metaheuristics
for the optimisation of water resources systems has been an active research field for over …

We propose a surrogate-assisted reference vector guided evolutionary algorithm (EA) for
computationally expensive optimization problems with more than three objectives. The …

Recent decades have witnessed great advancements in multiobjective evolutionary
algorithms (MOEAs) for multiobjective optimization problems (MOPs). However, these …

Evolutionary algorithms are widely used for solving multiobjective optimization problems but
are often criticized because of a large number of function evaluations needed …

Evolutionary multiobjective optimization has been a research area since the mid-1980s, and
has experienced a very significant activity in the last 20 years. However, and in spite of the …

In today's competitive environment, new turbomachinery designs need to be not only more
efficient, quieter, and “greener” but also need to be developed at on much shorter time …

Many-objective optimization problems (MaOPs) contain four or more conflicting objectives to
be optimized. A number of efficient decomposition-based evolutionary algorithms have been …

Thirty years, 1993–2023, is a huge time frame in science. We address some major
developments in the field of evolutionary algorithms, with applications in parameter …

In dealing with the expensive multiobjective optimization problem, some algorithms convert
it into a number of single-objective subproblems for optimization. At each iteration, these …