Dendritic Cell Algorithm with Bayesian Optimization Hyperband for Signal Fusion

The dendritic cell algorithm (DCA) is an excellent prototype for developing Machine Learning inspired by the function of the powerful natural immune system. Too many parameters increase complexity and lead to plenty of criticism in the signal fusion procedure of DCA. The loss function of DCA is ambiguous due to its complexity. To reduce the uncertainty, several researchers simplified the algorithm program; some introduced gradient descent to optimize parameters; some utilized searching methods to find the optimal parameter combination. However, these studies are either time-consuming or need to be revised in the case of non-convex functions. To overcome the problems, this study models the parameter optimization into a black-box optimization problem without knowing the information about its loss function. This study hybridizes bayesian optimization hyperband (BOHB) with DCA to propose a novel DCA version, BHDCA, for accomplishing parameter optimization in the signal fusion process. The BHDCA utilizes the bayesian optimization (BO) of BOHB to find promising parameter configurations and applies the hyperband of BOHB to allocate the suitable budget for each potential configuration. The experimental results show that the proposed algorithm has significant advantages over the other DCA expansion algorithms in terms of signal fusion.