Experimental Analysis of Design Elements of Scalarizing Functions-based Multiobjective Evolutionary Algorithms
This work provides incremental insights for researchers and practitioners in multiobjective optimization by clarifying which algorithm design elements most affect performance in specific combinatorial problems.
The paper systematically analyzes the influence of design elements in scalarizing functions-based multiobjective evolutionary algorithms (MOEAs), such as MOGLS and MOEA/D, on performance across three combinatorial optimization problems, finding that parent selection is the key differentiating factor while weight vector selection has negligible impact with sufficient uniform vectors.
In this paper we systematically study the importance, i.e., the influence on performance, of the main design elements that differentiate scalarizing functions-based multiobjective evolutionary algorithms (MOEAs). This class of MOEAs includes Multiobjecitve Genetic Local Search (MOGLS) and Multiobjective Evolutionary Algorithm Based on Decomposition (MOEA/D) and proved to be very successful in multiple computational experiments and practical applications. The two algorithms share the same common structure and differ only in two main aspects. Using three different multiobjective combinatorial optimization problems, i.e., the multiobjective symmetric traveling salesperson problem, the traveling salesperson problem with profits, and the multiobjective set covering problem, we show that the main differentiating design element is the mechanism for parent selection, while the selection of weight vectors, either random or uniformly distributed, is practically negligible if the number of uniform weight vectors is sufficiently large.