Today, I will talk about the importance of using standard terminology in research papers in computer science. The idea to talk about this on the blog came after reading an interesting letter about research on optimization called “Metaphor‑based metaheuristics, a call for action: the elephant in the room” by Aranha et al. (DOI: 10.1007/s11721-021-00202-9).
This paper explains that in the field of optimization, there have been a growing list of articles in the last decade proposing seemingly new approach for optimization but explained using a wide range of metaphors some related to animals (e.g. bats, grey wolves, termites, spiders), natural phenomena (e.g. invasive weed, the big bang, river erosion), and many other weird sources of inspirations (e.g. how musicians play music together, how interior design is carried and the political behavior of countries).
A key issue pointed by the authors and other researchers is that many metaphor-based optimization algorithms introduce new terminology that are unnecessary to explain the new algorithms, as they could be explained more simply using the existing terminology. For example, it was shown by Camacho-Villalon (DOI: 10.1007/s11721-019-00165-y) that some optimization algorithms such as Intelligent Water Drop (IWD) optimization are nothing but a special case of Ant Colony Optimization (ACO). However, the terminology is changed and pheromone in ACO is now called the soil in IWD, and ants are water drops, and so on. Another example is black hole optimization, which was shown to be a special case of particle swarm optimization.
The main problem with authors proposing seemingly new algorithms using non standard terminology is as Aranha explains: ” (i) creating confusion in the literature, (ii) hindering our understanding of the existing metaphor-based metaheuristics, and (iii) making extremely difficult to compare metaheuristics both theoretically and experimentally.”
This problem has become quite big in optimization research with several papers proposing new metaphors that are unrealistic or unnecessary to explain small modifications to existing algorithms, so as to publish more papers with little innovation. However, this problem also appears in other fields of computer science where researchers use non standard terminology in their papers. As a result, it often become difficult to verify where an idea truly came from, some work may be duplicated, and finding other papers related to an idea can become quite difficult (if several papers use different terminology.
This is why, it is important to always use standard terminology when proposing a new paper, and also to clearly indicate the relationship with previous papers, and give credit when credit is due. This helps the research community in making it easier to find papers and understanding the relationships between them.
Hope that this has been an interesting blog post. If you have time, you may read the above papers that I have mentioned. They are quite interesting and highlight this issue.
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Philippe Fournier-Viger is a distinguished professor working in China and founder of the SPMF open source data mining software.
