Seeding the Initial Population of Multi-Objective Evolutionary Algorithms: A Computational Study

Graphical abstractDisplay Omitted HighlightsWe study the benefits of seeding for multi-objective optimization algorithms.We investigate two approaches for five state-of-the-art algorithms on 48 functions.Different optimization algorithms benefit very differently from seeding.AGE and SMS-EMOA typically achieve the best approximation of the true Pareto front. Most experimental studies initialize the population of evolutionary algorithms with random genotypes. In practice, however, optimizers are typically seeded with good candidate solutions either previously known or created according to some problem-specific method. This seeding has been studied extensively for single-objective problems. For multi-objective problems, however, very little literature is available on the approaches to seeding and their individual benefits and disadvantages. In this article, we are trying to narrow this gap via a comprehensive computational study on common real-valued test functions. We investigate the effect of two seeding techniques for five algorithms on 48 optimization problems with 2, 3, 4, 6, and 8 objectives. We observe that some functions (e.g., DTLZ4 and the LZ family) benefit significantly from seeding, while others (e.g., WFG) profit less. The advantage of seeding also depends on the examined algorithm.