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

When addressing constrained multiobjective optimization problems (CMOPs) via
evolutionary algorithms, various constraints and multiple objectives need to be satisfied and …

With the remarkable progress of cooperative communication technology in recent years, its
transformation to vehicular networking is gaining momentum. Such a transformation has …

Dynamic multiobjective optimization problems (DMOPs) challenge multiobjective
evolutionary algorithms (MOEAs) because those problems change rapidly over time. The …

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

Abstract Dynamic Multi-objective Optimization is a challenging research topic since the
objective functions, constraints, and problem parameters may change over time. Although …

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

In addition to the search for feasible solutions, the utilization of informative infeasible
solutions is important for solving constrained multiobjective optimization problems (CMOPs) …

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

In addition to the need for simultaneously optimizing several competing objectives, many
real-world problems are also dynamic in nature. These problems are called dynamic multi …