Is perturbation an effective restart strategy?
This work addresses a specific bottleneck in optimization algorithms for researchers and practitioners, but it appears incremental as it builds on existing restart strategy concepts.
The paper tackles the problem of determining optimal perturbation step sizes in restart strategies for search algorithms by introducing a new property called 'Neighbours with Similar Fitness' and demonstrates its impact on restart effectiveness.
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.