A New Diversity Maintenance Strategy Based On The Double Granularity Grid For Multiobjective Optimization

The diversity maintenance of nondominated solutions is crucial for solving multiobjective optimization problems. The grid strategy is a very effective way to maintain the diversity of nodominated solutions, but the existing grid strategies all adopt single-layer grid structure, which has weak ability for judging the distribution of nodominated solutions in the hyperboxes with the same crowding degree. To further explore the ability of the grid strategy for maintaining the diversity of nondominated solutions, this paper presents a new diversity maintenance strategy based on the double granularity grid. The double granularity grid strategy firstly partitions the hyperboxes with the same largest crowding degree into fine granularity hyperboxes. Then, it selects nondominated individual solutions according to the solution distribution in both coarse and fine granularity hyperboxes, which can avoids randomness for selecting individual solutions in the single grid structure. To validate the performance of the double granularity grid strategy, we first integrated it with two famous algorithms, then tested the two integration algorithms by comparing them with the original algorithms and four other state-of-the-art algorithms.The experimental results validate the powerful advantages of the proposed double granularity grid strategy.