# Effective anytime algorithm for multiobjective combinatorial optimization problems

arXiv: https://arxiv.org/abs/2403.08807 · HTML: https://mlsanity.com/paper/2403.08807/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 1 of its 38 references that are in the map.
- First posted on arXiv 6 Feb 2024.

## Builds on (most cited first, 1 of 1)

- [A linear bound on the number of scalarizations needed to solve discrete tricriteria optimization problems](https://mlsanity.com/paper/1305.5266/) (2013)

## Abstract (arXiv)

In multiobjective optimization, the result of an optimization algorithm is a set of efficient solutions from which the decision maker selects one. It is common that not all the efficient solutions can be computed in a short time and the search algorithm has to be stopped prematurely to analyze the solutions found so far. A set of efficient solutions that are well-spread in the objective space is preferred to provide the decision maker with a great variety of solutions. However, just a few exact algorithms in the literature exist with the ability to provide such a well-spread set of solutions at any moment: we call them anytime algorithms. We propose a new exact anytime algorithm for multiobjective combinatorial optimization combining three novel ideas to enhance the anytime behavior. We compare the proposed algorithm with those in the state-of-the-art for anytime multiobjective combinatorial optimization using a set of 480 instances from different well-known benchmarks and four different performance measures: the overall non-dominated vector generation ratio, the hypervolume, the general spread and the additive epsilon indicator. A comprehensive experimental study reveals that our proposal outperforms the previous algorithms in most of the instances.