MOEA/D with Random Partial Update Strategy
This work addresses an incremental improvement in multi-objective evolutionary algorithms for optimization researchers, focusing on resource allocation efficiency.
The paper tackles the problem of improving the performance of the MOEA/D algorithm by introducing a random partial update strategy for resource allocation, resulting in enhanced HV and IGD values and a higher proportion of non-dominated solutions, especially with fewer updates per iteration.
Recent studies on resource allocation suggest that some subproblems are more important than others in the context of the MOEA/D, and that focusing on the most relevant ones can consistently improve the performance of that algorithm. These studies share the common characteristic of updating only a fraction of the population at any given iteration of the algorithm. In this work we investigate a new, simpler partial update strategy, in which a random subset of solutions is selected at every iteration. The performance of the MOEA/D using this new resource allocation approach is compared experimentally against that of the standard MOEA/D-DE and the MOEA/D with relative improvement-based resource allocation. The results indicate that using the MOEA/D with this new partial update strategy results in improved HV and IGD values, and a much higher proportion of non-dominated solutions, particularly as the number of updated solutions at every iteration is reduced.