Evolutionary Large-scale Sparse Multi-objective Optimization for Collaborative Edge-cloud Computation Offloading

Guang Peng, Huaming Wu, Han Wu, Katinka Wolter


This paper proposes evolutionary large-scale sparse multi-objective optimization (ELSMO) algorithms for collaboratively solving edge-cloud computation offloading problems. To begin with, a collaborative edge-cloud computation offloading multi-objective optimization model is established in a mobile environment, where the offloading decision is represented as a binary encoding. Considering the large-scale and sparsity property of the computation offloading model, the restricted Boltzmann machine (RBM) is applied to reduce the dimensionality and learn the Pareto-optimal subspace. In addition, the contribution score of each decision variable is assumed to generate new offsprings. Combining the RBM and the contribution score, two evolutionary algorithms using non-dominated sorting and crowding distance methods are designed, respectively. The proposed algorithms are compared with other state-of-the-art algorithms and offloading strategies on a number of test problems with different scales. The experiment results demonstrate the superiority of the proposed algorithms.


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