Research orientation and novelty discriminant for new metaheuristic algorithms

The rapid rate of generating a new metaheuristic algorithm almost every month is causing increasing concerns and disputes about their novelty. To stop the disputes and steer algorithm design in a healthy direction, this article presents a discriminant method of novelty and a research orientation for metaheuristic algorithms. The novelty discriminant is implemented by two novel mathematical definitions of homologous algorithms and root algorithms. The two definitions are developed to divide algorithms into two classes according to a discrepancy in whether the reproduction operator of an algorithm is a linear combination of existing operators. Root algorithms are strongly innovative because of the novelty of their reproduction operators. A homologous algorithm is recognized as a novel algorithm only when the practical value and academic significance of the new combinatorial structure of the algorithm's reproduction operator is clearly highlighted. So a research orientation that the study of a homologous algorithm should focus on how a certain metaphor evokes a new combinatorial structure can be developed. Moreover, numerical experiments should be conducted to analyze the relationship between its search behavior and its new combinatorial structure. Further work can be directed towards studying the systematization of existing knowledge about search behaviors of metaheuristic algorithms.