  Different from most other dynamic multi-objective optimization problems
(DMOPs), DMOPs with a changing number of objectives usually result in expansion
or contraction of the Pareto front or Pareto set manifold. Knowledge transfer
has been used for solving DMOPs, since it can transfer useful information from
solving one problem instance to solve another related problem instance.
However, we show that the state-of-the-art transfer algorithm for DMOPs with a
changing number of objectives lacks sufficient diversity when the fitness
landscape and Pareto front shape present nonseparability, deceptiveness or
other challenging features. Therefore, we propose a knowledge transfer dynamic
multi-objective evolutionary algorithm (KTDMOEA) to enhance population
diversity after changes by expanding/contracting the Pareto set in response to
an increase/decrease in the number of objectives. This enables a solution set
with good convergence and diversity to be obtained after optimization.
Comprehensive studies using 13 DMOP benchmarks with a changing number of
objectives demonstrate that our proposed KTDMOEA is successful in enhancing
population diversity compared to state-of-the-art algorithms, improving
optimization especially in fast changing environments.
