Enhanced open-source scatter search algorithm for solving quadratic unconstrained binary optimization problems

Abstract

In recent years, quantum computing has driven significant excitement and innovation, with the Quadratic Unconstrained Binary Optimization (QUBO) model at its core. This paper introduces SATPR, a new open-source quantum-inspired metaheuristic algorithm that combines scatter search, adaptive tenure tabu search, and path-relinking. The adaptive nature of the tabu tenure, achieved through the integration of various heuristic components, enables SATPR to effectively solve different types of QUBO problem instances. Additionally, SATPR utilizes parallelism to fully leverage multi-threading capabilities, further enhancing its computational efficiency. We conducted extensive evaluations on large and challenging problem instances from four benchmark sets, including well-known QUBO and Max-Cut instances, as well as less explored random graph structures. Our results demonstrate that SATPR is highly competitive in both solution quality and computational efficiency when compared with leading metaheuristic QUBO solvers and the quantum-inspired Fixstars Amplify Annealing Engine.