Differential Evolution (DE) is arguably one of the most powerful and versatile evolutionary
optimizers for the continuous parameter spaces in recent times. Almost 5 years have passed …

Evolutionary algorithms have been shown to be very successful in solving multi-objective
optimization problems (MOPs). However, their performance often deteriorates when solving …

Decomposition is a well-known strategy in traditional multiobjective optimization. However,
the decomposition strategy was not widely employed in evolutionary multiobjective …

Although synthetic test problems are widely used for the performance assessment of
evolutionary multi-objective optimization algorithms, they are likely to include unrealistic …

While Pareto-based multiobjective optimization algorithms continue to show effectiveness
for a wide range of practical problems that involve mostly two or three objectives, their …

In recent decades, the field of multiobjective optimization has attracted considerable interest
among evolutionary computation researchers. One of the main features that makes …

Humans have the ability to identify recurring patterns in diverse situations encountered over
a lifetime, constantly understanding relationships between tasks and efficiently solving them …

Multiobjective evolutionary algorithms based on decomposition (MOEA/D) have attracted
tremendous attention and achieved great success in the fields of optimization and decision …

In multiobjective optimization, a scalable test problem is one that can be formulated for an
arbitrary number of objectives. Scalable test problems evaluate the conceptual foundations …

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 …