Designing New Metaheuristics: Manual Versus Automatic Approaches

Abstract

A metaheuristic is a collection of algorithmic concepts that can be used to define heuristic methods applicable to a wide set of optimization problems for which exact/analytical approaches are either limited or impractical. In other words, a metaheuristic can be considered a general algorithmic framework that can be easily adapted to different optimization problems. In this article, we discuss the two main approaches used to create new metaheuristics: manual design, which is based on the designer’s “intuition” and often involves looking for inspiration in other fields of knowledge, and automatic design, which seeks to reduce human involvement in the design process by harnessing recent advances in automatic algorithm configuration methods. In this context, we discuss the trend of manually designed “novel” metaphor-based metaheuristics inspired by natural, artificial, and even supernatural behaviors. In recent years, this trend has been strongly criticized due to the uselessness of new metaphors in devising truly novel algorithms and the confusion such metaheuristics have created in the literature. We then present automatic design as a powerful alternative to manual design that has the potential to render the “novel” metaphor-based metaheuristics trend obsolete. Finally, we examine several fundamental aspects of the field of metaheuristics and offer suggestions for improving them.