Multi- and many-objective factored evolutionary algorithms

Abstract

Multi-Objective Optimization (MOO) is the problem of optimizing two or more competing objectives, where problems dealing with more than three competing objectives are termed as Many-Objective (MaOO). Such problems occur naturally in the real world. For example, many engineering design problems have to deal with competing objectives, such as cost versus quality in product design. How do we handle these competing objectives? To answer this question, population-based meta-heuristic algorithms that find a set of Pareto optimal solutions have become a popular approach. However, with the increase in complexity of problems, a single population approach may not be the most efficient to solve MOO problems. For this reason, co-operative co-evolutionary algorithms (CCEA) are used, which split the population into subpopulations optimizing over subsets of variables that can now be optimized simultaneously. Factored Evolutionary Algorithms (FEA) extends CCEA by including overlap in the subpopulations. This dissertation extends FEA to MOO, thus creating the Multi-Objective FEA (MOFEA). We apply MOFEA to different problems in the MOO family with positive results; these problems include combinatorial and continuous benchmarks as well as problems in the real-world domain of Precision Agriculture. Furthermore, we investigate the influence of different grouping techniques on continuous large-scale, MOO, and MaOO problems to help guide research to use the appropriate techniques for specific problems. Based on these results, we find that some MaOO problems lead to large sets of non-dominated solutions. From this, an Objective Archive Management (OAM) strategy is presented that creates separate archives for each objective based on performance and diversity criteria. OAM successfully reduces large solution sets to a more manageable size to help end-users make more informed decisions. The presented research makes four main contributions to the field of Computer Science: the creation of a new Multi-Objective framework to create and use subpopulation in a co-operative manner including the ability to use overlapping populations, the analysis of different grouping strategies and their influence on continuous optimization in both large- scale and multi-objective optimization, the introduction of a post-optimization solution set reduction approach, and the inclusion of an environmental objective into a real-world Precision Agriculture application.