Applications Of Multi-Objective Evolutionary Algorithms

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Multiobjective evolutionary algorithms to identify highly autocorrelated areas: the case of spatial distribution in financially compromised farms (2014, methodology): “During this process, both the weak and strict Pareto optimality criteria (Coello-Coello and Lamont 2004) are used.”
A modified NSGA-II solution for a new multi-objective hub maximal covering problem under uncertain shipments (2014, methodology): “Early analogies between the mechanism of natural selection and learning or optimization process have been led to development of so-called evolutionary algorithms whose main goal is to simulate the evolutionary process on a computer (Coello Coello and Lamont 2004).”
Multi-objective grey wolf optimizer: A novel algorithm for multi-criterion optimization (2016, background): “Nowadays, the application of multibjective optimization techniques can be found in different fields f studies: mechanical engineering (Kipouros et al., 2008), civil enineering (Luh & Chueh, 2004), chemistry (Gaspar-Cunha & Covas, 004; Rangaiah, 2008), and other fields (Coello & Lamont, 2004).”