Decomposition-based multiobjective evolutionary algorithms (MOEAs) decompose a
multiobjective optimization problem into a set of scalar objective subproblems and solve …

Convergence analysis of multi-objective optimization algorithm has been an area of vital
interest to the research community. With this regard, a number of approaches have been …

Recently, evolutionary algorithms have made great achievements in multi-objective
optimization problems (MOPs), but there is a little research on how to deal with noisy multi …

The chance-constrained knapsack problem is a variant of the classical knapsack problem
where each item has a weight distribution instead of a deterministic weight. The objective is …

In this article, we propose an adaptively weighted yield-driven EM optimization technique
incorporating neurotransfer function (neuro-TF) surrogate. In the proposed technique, an …

Evolutionary algorithms have been widely used for a range of stochastic optimization
problems in order to address complex real-world optimization problems. We consider the …

Addressing a complex real-world optimization problem is a challenging task. The chance-
constrained knapsack problem with correlated uniform weights plays an important role in the …

Evolutionary algorithms have been widely used for a range of stochastic optimization
problems. In most studies, the goal is to optimize the expected quality of the solution …

Tackling uncertainty is becoming increasingly relevant for decision-support across fields due
to its critical impact on real-world problems. Uncertainty is often modelled using scenarios …

Evolutionary multi-objective algorithms have successfully been used in the context of Pareto
optimization where a given constraint is relaxed into an additional objective. In this paper …