Restricted Boltzmann machine based algorithm for multi-objective optimization

Restricted Boltzmann machine is an energy-based stochastic neural network with unsupervised learning. This network consists of a layer of hidden unit and visible unit in an undirected generative network. In this paper, restricted Boltzmann machine is modeled as estimation of distribution algorithm in the context of multi-objective optimization. The probabilities of the joint configuration over the visible and hidden units in the network are trained until the distribution over the global state reach a certain degree of thermal equilibrium. Subsequently, the probabilistic model is constructed using the energy function of the network. Moreover, the proposed algorithm incorporates clustering in phenotype space and other canonical operators. The effects on the stability of the trained network and clustering in optimization are rigorously examined. Experimental investigations are conducted to analyze the performance of the algorithm in scalable problems with high numbers of objective functions and decision variables.