Parallel Processing for Dynamic Multi-Objective Optimization


The main objective of this PhD thesis is to advance the field of parallel multi-objective evolutionary algorithms to solve dynamic multi-objective optimization problems. Thus, the research presented in this thesis involves three different, although related, fields: * Multi-objective evolutionary algorithms (MOEAs), * Dynamic multi-objective optimization (DMO) problems, and * Parallelization of MOEAs to solve DMO problems. The degree of advancement of the research varies for each of the aforementioned topics, from a full-fledged research field as it is the MOEA topic to a new emerging subject as it happens with dynamic multi-objective optimization. Nevertheless, proposals to improve further the three afore-mentioned subjects have been made in this thesis. Firts of all, this thesis introduces a low-cost MOEA able to deal with multi-objective problems within more restrictive time limits than other state-of-the-art can do. Secondly, the field of dynamic optimization is reviewed and some additions are made so that the field moves forward to tackle dynamic multi-objective problems. This has been facilitated by the introduction of performance measures for problems that are both dynamic and multi-objective. Moreover, modifications are proposed for two of the five de facto standard test cases for DMO problems. Thirdly, the parallelization of MOEAs to solve DMO problems is adressed with two different proposed approaches: * A hybrid master-worker and island approach called pdMOEA, and * A fully distributed approach called pdMOEA+. These two approaches are compared side-by-side with the test cases already mentioned. Finally, future work to follow upon the achievements of this thesis is outlined.