Multiobjective Evolutionary Component Effect on Algorithm behavior
Yuri Lavinas, Marcelo Ladeira, Gabriela Ochoa, Claus Aranha

TL;DR
This paper investigates how different components of automatically designed multiobjective evolutionary algorithms influence their performance across various problem types, using a new methodology to analyze their behavior.
Contribution
It introduces a methodology to analyze the effects of algorithm components on performance, applied to a tuned MOEA/D on diverse constrained problems.
Findings
Algorithms converged quickly on analytical problems with good hypervolume values.
Diverse search trajectories observed in artificial problems, indicating varied exploration.
Performance varied across problem types, with some algorithms still improving at the end of runs.
Abstract
The performance of multiobjective evolutionary algorithms (MOEAs) varies across problems, making it hard to develop new algorithms or apply existing ones to new problems. To simplify the development and application of new multiobjective algorithms, there has been an increasing interest in their automatic design from their components. These automatically designed metaheuristics can outperform their human-developed counterparts. However, it is still unknown what are the most influential components that lead to performance improvements. This study specifies a new methodology to investigate the effects of the final configuration of an automatically designed algorithm. We apply this methodology to a tuned Multiobjective Evolutionary Algorithm based on Decomposition (MOEA/D) designed by the iterated racing (irace) configuration package on constrained problems of 3 groups: (1) analytical…
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Taxonomy
TopicsAdvanced Multi-Objective Optimization Algorithms · Metaheuristic Optimization Algorithms Research · Evolutionary Algorithms and Applications
