In recent years, the research community has witnessed an explosion of literature dealing
with the mimicking of behavioral patterns and social phenomena observed in nature towards …

When solving dynamic multiobjective optimization problems (DMOPs) by evolutionary
algorithms, the historical moving directions of some special points along the Pareto front …

Multiobjective evolutionary algorithms (MOEAs) have proven their effectiveness and
efficiency in solving problems with two or three objectives. However, recent studies show …

Abstract Ground Penetrating Radar is a complex nondestructive evaluation technique where
the antenna is the most critical part. The antenna is responsible for transmission and …

Ant colony optimization (ACO) algorithm is one of the most popular swarm-based algorithms
inspired by the behavior of an ant colony to find the shortest path for food. The multi …

Dynamic multiobjective optimization problem (DMOP) denotes the multiobjective
optimization problem, which contains objectives that may vary over time. Due to the …

Dynamic multiobjective optimization problem (DMOP) denotes the multiobjective
optimization problem which varies over time. As changes in DMOP may exist some patterns …

Dynamic multiobjective optimization problems (DMOPs) aim to optimize multiple (often
conflicting) objectives that are changing over time. Recently, there are a number of …

Surrogate-assisted meta-heuristic algorithms have won more and more attention for solving
computationally expensive problems over past decades. However, most existing surrogate …

Mastering the complexity of safety assurance for modern, software-intensive systems is
challenging in several domains, such as automotive, robotics, and avionics. Model-based …