UCCAAUniversity of California Center for Animal Alternatives
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To evaluate the performance of the proposed design method, we compare the best trade-off solutions returned by MOEA/D-DE with those obtained with state-of-the-art single-objective algorithms like CLPSO (Comprehensive Learning based PSO) [25], DEGL (DE with Global and Local Neighborhoods) [26] and also with the performance of an UCCAA designed following the method of Dessouky et al.
In Table 5 we also show the design objectives obtained in case of a uniformly spaced and uniformly excited CCAA (marked as UCCAA).
Objectives MOEA/D-DE DEGL CLPSO NSGA2 MODE UCCAA BW (Degrees) 22.06 22.10 23.38 22.14 22.10 28.83 SLL -25.31 -20.67 -24.70 -24.05 -22.57 -15.55 Table 6.