Comparison of Frameworks for Parallel Multiobjective Evolutionary Optimization in Dynamic Problems

In this chapter some alternatives are discussed to take advantage of parallel computers in dynamic multi-objective optimization problems (DMO) using evolutionary algorithms. In DMO problems, the objective functions, the constraints, and hence, also the solutions, can change over time and usually demand to be solved online. Thus, high performance computing approaches, such as parallel processing, should be applied to these problems to meet the quality requirements within the given time constraints. Taking this into account, we describe two generic parallel frameworks for multi-objective evolutionary algorithms. These frameworks are used to compare the parallel processing performance of some multi-objective optimization evolutionary algorithms: our previously proposed algorithms, SFGA and SFGA2, in conjunction with SPEA2 and NSGA-II. We also propose a model to explain the benefits of parallel processing in multi-objective problems and the speedup results observed in our experiments.