FANG: Fast and Efficient Successor-State Generation for Heuristic Optimization on GPUs

Abstract

Many optimization problems (especially nonsmooth ones) are typically solved by genetic, evolutionary, or metaheuristic-based algorithms. However, these genetic approaches and other related papers typically assume the existence of a neighborhood or successor-state function N(x), where x is a candidate state. The implementation of such a function can become arbitrarily complex in the field of combinatorial optimization. Many N(x) functions for a huge variety of different domain-specific problems have been developed in the past to solve this general problem. However, it has always been a great challenge to port or realize these functions on a massively-parallel architecture like a Graphics Processing Unit (GPU). We present a GPU-based method called FANG that implements a generic and reusable N(x) for arbitrary domains in the field of combinatorial optimization. It can be customized to satisfy domain-specific requirements and leverages the underlying hardware in a fast and efficient way by construction. Moreover, our method has a high scalability with respect to the number of input states and the complexity of a single state. Measurements show significant performance improvements compared to traditional exploration approaches leveraging the CPU on our evaluation scenarios.

This work was funded by the Germany Federal Ministry for Economic Affairs and Energy (BMWi): Project BloGPV (grant number 01MD18001B).