Approaching Dynamic Multi-Objective Optimization Problems by Using Parallel Evolutionary Algorithms

Many real world optimization problems are dynamic. On the other hand, there are many optimization problems whose solutions must optimize several objectives that are in conflict. In these dynamic multi-objective problems the concept of optimum must be redefined, because instead of providing only one optimal solution, the procedures applied to these multi-objective optimization problems should obtain a set of non-dominated solutions (known as Pareto optimal solutions) that change with time. As evolutionary algorithms steer a population of solutions in a concurrent way by making use of cooperative searching techniques, it could be relatively direct to adapt these algorithms to obtain sets of Pareto optimal solutions. This contribution deals with parallel evolutionary algorithms on dynamic multi-objective optimization (DMO) problems. In this kind of problems, the speed of the reaction to changes is a quite important topic in the context of dynamic optimization, and high-performance computing approaches, such as parallel processing, should be applied to meet the given solution constraints and quality requirements.