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

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 …

This paper describes jMetalPy, an object-oriented Python-based framework for multi-
objective optimization with metaheuristic techniques. Building upon our experiences with the …

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 …

This paper investigates the path planning problem for unmanned surface vehicle (USV),
wherein the goal is to find the shortest, smoothest, most economical and safest path in the …

The coinciding development of multiobjective evolutionary algorithms (MOEAs) and the
emergence of complex problem formulation in the finance and economics areas has led to a …

Various real-world problems can be attributed to constrained multiobjective optimization
problems (CMOPs). Although there are various solution methods, it is still very challenging …

Recently, several meta-heuristics and evolutionary algorithms have been proposed for
tackling optimization problems. Such methods tend to suffer from degraded performance …

Large-scale multi-objective optimization problems (MOPs) that involve a large number of
decision variables, have emerged from many real-world applications. While 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 …