Searching for Local Pareto Optimal Solutions: A Case Study on Polygon-Based Problems
Yiping Liu, Hisao Ishibuchi, Yusuke Nojima, Naoki Masuyama, Yuyan Han · 2019
Local Pareto optimal solutions may exist in multi-modal multi-objective optimization problems. Traditional multi-objective evolutionary algorithms usually try to escape from local Pareto optima. However, these solutions may be good enough for the decision makers and are additional options if Pareto optimal solutions are infeasible. In this paper, we modify our previous double-niched evolutionary algorithm (DNEA) to search for local Pareto optimal solutions. The new version is termed as DNEA-L. We apply DNEA-L to 3- and 4-objective polygon-based problems with local Pareto optima. The experimental results show that DNEA-L is efficient to find a large number of local Pareto optimal solutions with good diversity.