Squeaky Wheel Optimization

TL;DR

Squeaky Wheel Optimization (SWO) is a general iterative approach that improves solutions by analyzing and re-prioritizing problem elements, demonstrated on scheduling and graph coloring problems.

Contribution

Introduces SWO, a novel iterative optimization technique combining greedy construction with analysis-driven re-prioritization, applicable across diverse domains.

Findings

Encouraging results on fiber-optic scheduling problems.

Effective on graph coloring challenges.

Demonstrates generality across different problem types.

Abstract

We describe a general approach to optimization which we term `Squeaky Wheel' Optimization (SWO). In SWO, a greedy algorithm is used to construct a solution which is then analyzed to find the trouble spots, i.e., those elements, that, if improved, are likely to improve the objective function score. The results of the analysis are used to generate new priorities that determine the order in which the greedy algorithm constructs the next solution. This Construct/Analyze/Prioritize cycle continues until some limit is reached, or an acceptable solution is found. SWO can be viewed as operating on two search spaces: solutions and prioritizations. Successive solutions are only indirectly related, via the re-prioritization that results from analyzing the prior solution. Similarly, successive prioritizations are generated by constructing and analyzing solutions. This `coupled search' has some interesting properties, which we discuss. We report encouraging experimental results on two domains, scheduling problems that arise in fiber-optic cable manufacturing, and graph coloring problems. The fact that these domains are very different supports our claim that SWO is a general technique for optimization.