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A Chemist's guide to multi-objective optimization solvers for reaction optimization
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DOI：10.1039/d4re00175c.png)
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Recently, multi-objective optimization has garnered significant attention in the field of reaction optimization. Various multi-objective optimization solvers, such as MVMOO, EDBO+, Dragonfly, TSEMO, and EIM-EGO, have been developed and applied in real scenarios. However, the question of which solver to use persists, given that each problem is unique in terms of variables-be they continuous or categorical-and requires specific features, such as constraint handling and the capability for parallel evaluation. Although these solvers have been individually verified in real scenarios, a comparative analysis of their features and performance is lacking. This work focuses on assisting chemists in identifying the most suitable solver that best suits their problems, alongside a comparison of the different solvers' performances. For this purpose, the solvers were tested across 10 different chemical reaction-based in silico models, employing three metrics for performance comparison: hypervolume, modified generational distance, and worst attainment surface. This paper presents a chemist's guide for selecting multi-objective optimization algorithms for reaction optimization.
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