Exposing the grey wolf, moth‐flame, whale, firefly, bat, and antlion algorithms: six misleading optimization techniques inspired by bestial metaphors

Abstract

AbstractWe present a rigorous, component‐based analysis of six widespread metaphor‐based algorithms for tackling continuous optimization problems. In addition to deconstructing the six algorithms into their components and relating them with equivalent components proposed in well‐established techniques, such as particle swarm optimization and evolutionary algorithms, we analyze the use of the metaphors that inspired these algorithms to understand whether their usage has brought any novel and useful concepts to the field of metaheuristics. Our result is that the ideas proposed in the six studied algorithms have been in the literature of metaheuristics for years and that the only novelty in these self‐proclaimed novel algorithms is six different terminologies derived from the use of new metaphors. We discuss the reasons why the metaphors that inspired these algorithms are misleading and ultimately useless as a source of inspiration to design effective optimization tools. Finally, we discuss the rationale often presented by the authors of metaphor‐based algorithms as their motivation to propose more algorithms of this type, which is based on a wrong understanding of the no‐free‐lunch theorems for optimization.