Fitness Landscape-Based Characterisation of Nature-Inspired Algorithms
This work addresses algorithm selection for continuous optimization problems, though it appears incremental as it extends existing discrete-focused techniques to continuous domains.
The paper tackled the challenge of predicting which nature-inspired algorithms perform best on specific problem types by developing a fitness landscape analysis approach for continuous optimization. They compared six algorithms and identified which methods performed best on landscapes with particular features.
A significant challenge in nature-inspired algorithmics is the identification of specific characteristics of problems that make them harder (or easier) to solve using specific methods. The hope is that, by identifying these characteristics, we may more easily predict which algorithms are best-suited to problems sharing certain features. Here, we approach this problem using fitness landscape analysis. Techniques already exist for measuring the "difficulty" of specific landscapes, but these are often designed solely with evolutionary algorithms in mind, and are generally specific to discrete optimisation. In this paper we develop an approach for comparing a wide range of continuous optimisation algorithms. Using a fitness landscape generation technique, we compare six different nature-inspired algorithms and identify which methods perform best on landscapes exhibiting specific features.