Considerations in engineering parallel multiobjective evolutionary algorithms
Developing multiobjective evolutionary algorithms (MOEAs) involves thoroughly addressing the issues of efficiency and effectiveness. Once convinced of an MOEA's effectiveness the researcher often desires to reduce execution time and/or resource expenditure, which naturally leads to considering the MOEA's parallelization. Parallel MOEAs (pMOEAs) or distributed MOEAs are relatively new developments with few associated publications. pMOEA creation is not a simple task, involving analyzing various parallel paradigms and associated parameters. Thus, a thorough discussion of the major parallelized MOEA paradigms is included in this paper and succinct observations are made regarding an analysis of the current literature. Specifically, a previous MOEA notation is extended into the pMOEA domain to enable precise description and identification of various sets of interest. Innovative concepts for pMOEA migration, replacement and niching schemes are discussed, as well as presenting the first known generic pMOEA formulation. Taken together, this paper's analyses in conjunction with an original pMOEA design serve as a pedagogical framework and example of the necessary process to implement an efficient and effective pMOEA. Interspersed throughout the paper are various methods for creating and evaluating pMOEA implementations for those interested in extending the field's knowledge through further research. This aids the community in not only achieving a fuller understanding of parallelized MOEAs, but also identifying appropriate contexts for comparing their performance.
multiobjective evolutionary algorithms (moeas) parallel algorithm paradigms parallel multiobjective evolutionary algorithms (pmoeas) pareto front pareto optimality