A Fuzzy Adaptive Network With Learnable Parameters for Mixed-Integer Optimization

Abstract

In this article, we propose a fuzzy adaptive network (FAN) with learnable parameters for mixed-integer optimization. Specifically, by leveraging a recurrent network to infer the discretization parameters, the FAN is implemented in an easy-to-implement discrete-time format. FAN possesses the dynamic behavior of a high-precision numerical differential rule and maintains a simple network structure. In addition, a fuzzy mechanism is incorporated to adjust the step size. Sufficient conditions are derived such that the proposed FAN is globally exponentially convergent to a Karush–Kuhn–Tucker point. In the presence of nonconvexity in objective functions or constraints, multiple FANs operate concurrently in a hybrid intelligent algorithm. Finally, multiple comparative experiments are conducted to demonstrate the superiority of the proposed FAN in terms of time efficiency and solution quality.