Evolutionary dynamic multi-objective optimisation (EDMO) is a relatively young but rapidly
growing area of investigation. EDMO employs evolutionary approaches to handle multi …

Dynamic multiobjective optimization problems (DMOPs) are optimization problems with
multiple conflicting optimization objectives, and these objectives change over time. Transfer …

This paper presents a new algorithm, called steady-state and generational evolutionary
algorithm, which combines the fast and steadily tracking ability of steady-state algorithms …

Various real-world multiobjective optimization problems are dynamic, requiring evolutionary
algorithms (EAs) to be able to rapidly track the moving Pareto front of an optimization …

Dynamic multiobjective optimization problems (DMOPs) are characterized by optimization
functions that change over time in varying environments. The DMOP is challenging because …

The past decade has witnessed a growing amount of research interest in dynamic
multiobjective optimisation, a challenging yet very important topic that deals with problems …

A key issue in evolutionary algorithms for dynamic multi-objective optimization problems
(DMOPs) is how to detect and response environmental changes. Most existing evolutionary …

Existing studies on dynamic multiobjective optimization (DMO) focus on problems with time-
dependent objective functions, while the ones with a changing number of objectives have …

Dynamic multi-objective optimization problems (DMOPs), in which the environments change
over time, have attracted many researchers' attention in recent years. Since the Pareto set …

In real life, there are many dynamic multi-objective optimization problems which vary over
time, requiring an optimization algorithm to track the movement of the Pareto front (Pareto …