Real-world design problems in engineering and industry are usually multi-objective ormulticriteria. These multiple objectives often conflict with one another, which makes itimpossible to use any single design option without compromise. Common approachesare to provide good approximations to the true Pareto fronts of the problem of interest sothat decision makers can rank different options, depending on their preferences or theirutilities [1,5,15]. Compared with single-objective optimization, multi-objective optimization has its additional challenging issues such as time complexity, inhomogeneity, and dimensionality. It is usually more time consuming to obtain the true Pareto fronts because it typically requires us to produce many points on the Pareto front for good approximations.
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