An Evolutionary Ising Optimization Framework for Unconstrained Binary Quadratic Programming

An Ising machine (IM), as a type of analog computer tailored for tackling intractable combinatorial optimization problems, has attracted remarkable attention in recent years. In contrast to the blossoming field of bespoke IM hardware, developing metaheuristics from IMs remains largely uninvestigated...

Full description

Saved in:
Bibliographic Details
Published in:IEEE transactions on evolutionary computation p. 1
Main Authors: Fu, Wujie, Trivedi, Anupam, Chen, Guanyu, Gao, Yuan, Srinivasan, Dipti, Danner, Aaron
Format: Journal Article
Language:English
Published: IEEE 2025
Subjects:
Annealing
Computational efficiency
Computational modeling
Couplings
energy minimization
Heuristic algorithms
Ising model
Metaheuristics
Minimization
nature-inspired metaheuristic
Optimization
Physics
quadratic unconstrained binary optimization
Stationary state
ISSN:1089-778X, 1941-0026
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:An Ising machine (IM), as a type of analog computer tailored for tackling intractable combinatorial optimization problems, has attracted remarkable attention in recent years. In contrast to the blossoming field of bespoke IM hardware, developing metaheuristics from IMs remains largely uninvestigated. Here, we propose a physics-inspired evolutionary computation paradigm, termed the Ising optimization framework (IOF); it comprises a unique Ising algorithm and a hybrid annealing scheme, which together are well-suited for solving quadratic unconstrained binary optimization (QUBO) problems embedded in Ising system energy. The Ising algorithm leverages a set of iterated self-mapping functions to evolve an Ising-spin swarm, enabling efficient energy minimization in artificial Ising systems while mitigating detrimental chaos. Complementing the algorithm, a hybrid annealing scheme integrating singular value dropout, bifurcation control, and a nudging strategy, is devised to augment the overall optimization capacity. The effectiveness of the IOF is validated on various Ising and Max-cut problems with decision variables ranging from 625 to 5000 in number. In comparison to four major types of methods for solving QUBOs, including IM simulations, nature-inspired algorithms, a state-of-the-art heuristic, and the commercial solver Gurobi, the IOF consistently demonstrates notable optimization quality and computational efficiency. This paper provides a theoretical foundation and practical guidelines for bridging Ising-inspired approaches with evolutionary computation, offering an evolutionary perspective on Ising optimizations and suggesting a fertile avenue for future research and application.
ISSN:1089-778X
1941-0026
DOI:10.1109/TEVC.2025.3566963