MOEA/D with Random Partial Update Strategy

by   Yuri Lavinas, et al.

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.



There are no comments yet.


page 1

page 2

page 3

page 4


Faster Convergence in Multi-Objective Optimization Algorithms Based on Decomposition

The Resource Allocation approach (RA) improves the performance of MOEA/D...

Knowledge Transfer based Radio and Computation Resource Allocation for 5G RAN Slicing

To implement network slicing in 5G, resource allocation is a key functio...

Evolutionary framework for two-stage stochastic resource allocation problems

Resource allocation problems are a family of problems in which resources...

Architecture-Guided Test Resource Allocation Via Logic

We introduce a new logic named Quantitative Confidence Logic (QCL) that ...

Deep Learning for Radio Resource Allocation in Multi-Cell Networks

Increased complexity and heterogeneity of emerging 5G and beyond 5G (B5G...

Boosting Cooperative Coevolution for Large Scale Optimization with a Fine-Grained Computation Resource Allocation Strategy

Cooperative coevolution (CC) has shown great potential in solving large ...

Deep Partial Updating

Emerging edge intelligence applications require the server to continuous...
This week in AI

Get the week's most popular data science and artificial intelligence research sent straight to your inbox every Saturday.