Abstract
The challenges of many-objective optimization are
investigated; and one new algorithm, which is based on the
NSGA-II, is proposed for multi-objective optimization in this
paper. The reference points and an adaptable crossover rate are
combined in the algorithm to improve the performance of
NSGA-II. The performance of NSGA for optimizing the many
objective search space is examined with and without the
proposed algorithm through a constrained two-objective
problem with up to 40 dimensions. Simulation results show that
the proposed algorithm improves the performance of NSGA for
the selected test problem in generations where a non-dominated
set is not obtained by 39%.