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, more straightforward partial update strategy, in which a random subset of solutions is selected at every iteration. The performance of the MOEA/D-DE using this new resource allocation approach is compared experimentally against that of the standard MOEA/D-DE and the MOEA/D-DE with relative improvement-based resource allocation. The results indicate that using 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.
|Title of host publication||2020 IEEE Congress on Evolutionary Computation, CEC 2020 - Conference Proceedings|
|Number of pages||8|
|Publication status||Published - 3 Sep 2020|
|Event||2020 IEEE Congress on Evolutionary Computation - Glasgow, United Kingdom|
Duration: 19 Jul 2020 → 24 Jul 2020
|Conference||2020 IEEE Congress on Evolutionary Computation|
|Abbreviated title||CEC 2020|
|Period||19/07/20 → 24/07/20|
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- Multi-Objective Optimization
- Partial Update Strategy
- Resource Allocation