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 simulation-driven metaheuristic algorithms have been successful in solving numerous
problems compared to their deterministic counterparts. Despite this advantage, the …

In the past few decades, metaheuristics have demonstrated their suitability in addressing
complex problems over different domains. This success drives the scientific community …

The application of white box models in digital twins is often hindered by missing knowledge,
uncertain information and computational difficulties. Our aim was to overview the difficulties …

Most practical optimization problems are comprised of multiple conflicting objectives and
constraints which involve time-consuming simulations. Construction of metamodels of …

Optimal control theory provides insight into complex resource allocation decisions. The
forward–backward sweep method (FBSM) is an iterative technique commonly implemented …

Evolutionary multi-objective optimization (EMO) is certainly a story of great success
considering the numerous contributions and their applications to different problems and …

The Covid-19 disease has caused a world-wide pandemic with more than 60 million positive
cases and more than 1.4 million deaths by the end of November 2020. As long as effective …

Multi-objective optimization is an active field of research that has many applications. Owing
to its success and because decision-making processes are becoming more and more …

Most current multi-optimization survey papers classify methods into broad objective
categories and do not draw clear boundaries between the specific techniques employed by …