# A Random-Key Optimizer for Combinatorial Optimization

arXiv: https://arxiv.org/abs/2411.04293 · HTML: https://mlsanity.com/paper/2411.04293/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 6 of its 72 references that are in the map.
- First posted on arXiv 6 Nov 2024.

## Builds on (most cited first, 6 of 6)

- [ParamILS: An Automatic Algorithm Configuration Framework](https://mlsanity.com/paper/1401.3492/) (2014)
- [Biased Random-Key Genetic Algorithms: A Review](https://mlsanity.com/paper/2312.00961/) (2023)
- [Optimization of Robot Trajectory Planning with Nature-Inspired and Hybrid Quantum Algorithms](https://mlsanity.com/paper/2206.03651/) (2022)
- [Random-Key Cuckoo Search for the Travelling Salesman Problem](https://mlsanity.com/paper/1607.04324/) (2016)
- [Near-Optimal Disjoint-Path Facility Location Through Set Cover by Pairs](https://mlsanity.com/paper/1611.01210/) (2016)
- [A random-key GRASP for combinatorial optimization](https://mlsanity.com/paper/2405.18681/) (2024)

## Abstract (arXiv)

This paper introduces the Random-Key Optimizer (RKO), a versatile and efficient stochastic local search method tailored for combinatorial optimization problems. Using the random-key concept, RKO encodes solutions as vectors of random keys that are subsequently decoded into feasible solutions via problem-specific decoders. The RKO framework is able to combine a plethora of classic metaheuristics, each capable of operating independently or in parallel, with solution sharing facilitated through an elite solution pool. This modular approach allows for the adaptation of various metaheuristics, including simulated annealing, iterated local search, and greedy randomized adaptive search procedures, among others. The efficacy of the RKO framework, implemented in C++ and publicly available (Github public repository: github.com/RKO-solver), is demonstrated through its application to three NP-hard combinatorial optimization problems: the alpha-neighborhood p-median problem, the tree of hubs location problem, and the node-capacitated graph partitioning problem. The results highlight the framework's ability to produce high-quality solutions across diverse problem domains, underscoring its potential as a robust tool for combinatorial optimization.