Framework for identifying the equivalence between Nature-Inspired Metaheuristics
This addresses the issue of algorithm proliferation and criticism in the metaheuristic optimization domain, providing a method to assess novelty, though it is incremental as it builds on existing similarity concepts.
The paper tackles the problem of distinguishing novel nature-inspired metaheuristics from copies by defining a strong equivalence theorem based on cosine similarity of phenotypic and genotypic feature vectors, and develops a framework that shows achieving high similarity between well-known algorithms is hard or impossible in limited computational environments.
The domain of metaheuristic optimization has become vibrant due to a flood of new algorithms using a new nature-inspired metaphor but lacking clear methodological novelty. The Criticism behind the development of these algorithms has reached such an extent that the critics started to assert that all novel algorithms are only copies of already developed ones. In this study, we try to show that the situation is not so black and white. Therefore, we define a strong equivalence theorem for estimating the similarity between two nature-inspired metaheuristics, according to which two algorithms are equivalent if, and only if, the cosine similarity of their phenotypic and genotypic feature vectors, characterizing their behavior by searching for the optimal solutions, is above some threshold. On the theorem basis, a framework is developed for identifying the equivalence between nature-inspired metaheuristics. Extensive experimental work using the framework has shown that searching for conditions to achieve the high similarity of the more well-known nature-inspired metaheuristics is hard, or even not possible to achieve, in the limited computational environments.