In recent years, there has been a growing research interest in integrating machine learning
techniques into meta-heuristics for solving combinatorial optimization problems. This …

In the last few years, the formulation of real-world optimization problems and their efficient
solution via metaheuristic algorithms has been a catalyst for a myriad of research studies. In …

Metaheuristics are an impressive area of research with extremely important improvements in
the solution of intractable optimization problems. Major advances have been made since the …

Mathematical optimization is pervasive in all quantitative sciences. The ability to find good
parameters values in a generic numerical experiment while meeting complex constraints is …

The increasing complexity of real-world optimization problems raises new challenges to
evolutionary computation. Responding to these challenges, distributed evolutionary …

Research in metaheuristics for combinatorial optimization problems has lately experienced
a noteworthy shift towards the hybridization of metaheuristics with other techniques for …

Problems with multiple objectives arise in a natural fashion in most disciplines and their
solution has been a challenge to researchers for a long time. Despite the considerable …

Vehicle routing attributes are extra characteristics and decisions that complement the
academic problem formulations and aim to properly account for real-life application needs …

The field of parallel metaheuristics is continuously evolving as a result of new technologies
and needs that researchers have been encountering. In the last decade, new models of …

A variety of Reinforcement Learning (RL) techniques blends with one or more techniques
from Evolutionary Computation (EC) resulting in hybrid methods classified according to their …