  Population-based evolutionary algorithms have great potential to handle
multiobjective optimisation problems. However, these algorithms depends largely
on problem characteristics, and there is a need to improve their performance
for a wider range of problems. References, which are often specified by the
decision maker's preference in different forms, are a very effective method to
improve the performance of algorithms but have not been fully explored in
literature. This paper proposes a novel framework for effective use of
references to strengthen algorithms. This framework considers references as
search targets which can be adjusted based on the information collected during
the search. The proposed framework is combined with new strategies, such as
reference adaptation and adaptive local mating, to solve different types of
problems. The proposed algorithm is compared with state of the arts on a wide
range of problems with diverse characteristics. The comparison and extensive
sensitivity analysis demonstrate that the proposed algorithm is competitive and
robust across different types of problems studied in this paper.
