local dominance and controlling dominance area of solutions in multi and many objectives eas
This work presents local dominance with alignment of principle search direction and control of dominance area of solutions to enhance selection of MOEAs, aiming to improve their performance on multi and many objectives combinatorial problems. We show that the methods used independently can substantially improve either diversity or convergence. Also, by including control of dominance area of solutions within the local dominance algorithm, we show that diversity and convergence can improve simultaneously while reducing the computational cost of the algorithm.
local dominance control of dominance area of solutions selection