Performance differences between multi-objective evolutionary algorithms in different environments

Abstract: The time required to find the optimal solution to a problem increases exponentially as thesize and amount of parameters increases. Evolutionary algorithms tackle this problemheuristically by generating better solutions over time. When there is more than oneobjective in a problem, algorithms must generate multiple solutions to fit any preference inspecific objectives. As the amount of objectives increases, the effort required to generategood sets of solutions increases.This study investigated how increasing the amount of objectives impacted fourmulti-objective evolutionary algorithms differently. The algorithms were tested againsttwo different sets of problems with each problem being tested in twenty seven differentcircumstances. The results of these tests were summarized into two different statisticsbased on ranking used to determine if there was any performance changes.The results indicate that some multi-objective evolutionary algorithms havebetter performance against problems with more objectives. The underlying cause andmagnitude in performance difference was not identified.