Is perturbation an effective restart strategy?

TL;DR

This paper investigates the effectiveness of perturbation-based restart strategies in search algorithms, introducing a new landscape property to understand when such strategies are beneficial.

Contribution

It introduces the 'Neighbours with Similar Fitness' property and analyzes its impact on the success of perturbation restart strategies.

Findings

Effectiveness depends on the landscape property

Introduces a new landscape property for analysis

Provides insights into perturbation size selection

Abstract

Premature convergence can be detrimental to the performance of search methods, which is why many search algorithms include restart strategies to deal with it. While it is common to perturb the incumbent solution with diversification steps of various sizes with the hope that the search method will find a new basin of attraction leading to a better local optimum, it is usually not clear how big the perturbation step should be. We introduce a new property of fitness landscapes termed "Neighbours with Similar Fitness" and we demonstrate that the effectiveness of a restart strategy depends on this property.