{"type":"ScholarlyArticle","title":"Proven Approximation Guarantees in Multi-Objective Optimization: SPEA2 Beats NSGA-II","authors":[{"name":"Yasser Alghouass","slug":"yasser-alghouass","given":"Yasser","family":"Alghouass","affiliation":""},{"name":"Benjamin Doerr","slug":"benjamin-doerr","given":"Benjamin","family":"Doerr","affiliation":""},{"name":"Martin S. Krejca","slug":"martin-s-krejca","given":"Martin S.","family":"Krejca","affiliation":""},{"name":"Mohammed Lagmah","slug":"mohammed-lagmah","given":"Mohammed","family":"Lagmah","affiliation":""}],"abstract":"Together with the NSGA-II and SMS-EMOA, the strength Pareto evolutionary algorithm 2 (SPEA2) is one of the most prominent dominance-based multi-objective evolutionary algorithms (MOEAs). Different from the NSGA-II, it does not employ the crowding distance (essentially the distance to neighboring solutions) to compare pairwise non-dominating solutions but a complex system of σ-distances that builds on the distances to all other solutions. In this work, we give a first mathematical proof showing that this more complex system of distances can be superior. More specifically, we prove that a simple steady-state SPEA2 can compute optimal approximations of the Pareto front of the OneMinMax benchmark in polynomial time. The best proven guarantee for a comparable variant of the NSGA-II only assures approximation ratios of roughly a factor of two, and both mathematical analyses and experiments indicate that optimal approximations are not found efficiently.","doi":"10.24963/ijcai.2025/982","url":"https://ijcai.org/proceedings/2025/982","pdf":"https://ijcai.org/proceedings/2025/0982.pdf","markdown":"https://ijcai.org/proceedings/2025/982.md","bibtex":"https://ijcai.org/proceedings/2025/bibtex/982","volume":{"slug":"2025","title":"Proceedings of the Thirty-Fourth International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2025","url":"https://ijcai.org/proceedings/2025/","year":2025,"location":"Montreal, Canada","dates":"16-22 August 2025","editors":"James Kwok","isbn":"978-1-956792-06-5","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2025"},"section":"Search","track":"Main Track","number":982,"pages":{"first":8833,"last":8841},"keywords":["Search: S: Evolutionary computation"],"videos":[],"references":[],"license_note":"Copyright International Joint Conferences on Artificial Intelligence."}