Decomposition based multiobjective evolutionary algorithms approximate the Pareto front of a multiobjective optimization problem by optimizing a set of subproblems in a collaborative manner. Often, each subproblem is associated with a direction vector and a reference point. The settings of these parameters have a very critical impact on convergence and diversity of the algorithm. Some work has been done to study how to set and adjust direction vectors to enhance algorithm performance for particular problems. In contrast, little effort has been made to study how to use reference points for controlling diversity in decomposition based algorithms. In this paper, we first study the impact of the reference point setting on selection in decomposition based algorithms. To balance the diversity and convergence, a new variant of the multiobjective evolutionary algorithm based on decomposition with both the ideal point and the nadir point is then proposed. This new variant also employs an improved global replacement strategy for performance enhancement. Comparison of our proposed algorithm with some other state-of-theart algorithms is conducted on a set of multiobjective test problems. Experimental results show that our proposed algorithm is promising.
Recently, Wang et al. REF studied the effect of the reference point setting on the performance of decomposition-based algorithms for problems with either concave or convex PFs.
On the use of two reference points in decomposition based multiobjective evolutionary algorithms
Swarm Evol. Comput.
Swarm Evol. Comput.
Computer Science