Dynamic constrained multiobjective optimization problems (DCMOPs) abound in real-world
applications and gain increasing attention in the evolutionary computation community. To …

To promote research on dynamic constrained multiobjective optimization, we first propose a
group of generic test problems with challenging characteristics, including different modes of …

As a well-known stochastic optimization algorithm, the particle swarm optimization (PSO)
algorithm has attracted the attention of many researchers all over the world, which has …

The insights and benefits to be realised through the optimisation of multiple independent,
but conflicting objectives are well recognised by practitioners seeking effective and robust …

Multiobjective optimization problems with heterogeneous objectives are defined as those
that possess significantly different types of objective function components (not just …

Building on recent work by the authors, we consider the problem of performing
multiobjective optimization when the objective functions of a problem have differing …

Bayesian Optimization (BO) is a sequential optimization strategy initially proposed to solve
the single-objective black-box optimization problem that is costly to evaluate. Built on the …

Box-constraints limit the domain of decision variables and are common in real-world
optimization problems, for example, due to physical, natural or spatial limitations …

Building on previous work of the authors, this paper formally defines and reviews the first
approach, referred to as navigation, towards a common understanding of search and …

We consider a multiobjective optimization scenario in which one or more objective functions
may be subject to delays (or longer evaluation durations) relative to the other functions. We …