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In swarm robotics, self-organized aggregation refers to a collective process in which robots form a single aggregate in an arbitrarily chosen aggregation site among those available in the environment, or just in an arbitrarily chosen location. Instead of focusing exclusively on the formation of a single aggregate, in this study we discuss how to design a swarm of robots capable of generating a variety of final distributions of the robots to the available aggregation sites. We focus on an environment with two possible aggregation sites, A and B. Our study is based on the following working hypothesis: robots distribute on site A and B in quantities that reflect the relative proportion of robots in the swarm that selectively avoid A with respect to those that selectively avoid B. This is with an as minimal as possible proportion of robots in the swarm that selectively avoid one or the other site. We illustrate the individual mechanisms designed to implement the above mentioned working hypothesis, and we discuss the promising results of a set of simulations that systematically consider a variety of experimental conditions.

Taking inspiration from natural behaviors to devise new optimization algorithms has played an important role in the history of the field of metaheuristics (Sörensen et al. 2017). Unfortunately, in the last two decades we have been witnessing a new trend by which dozens of metaphor-based metaheuristics based on the most diverse possible set of natural, artificial, social, and sometimes even supernatural phenomena and behaviors are proposed, without a clear motivation beyond the desire of their authors to publish their papers. Despite several attempts (Sörensen 2015; Sörensen et al. 2019; Weyland 2010; Piotrowski et al. 2014; Fong et al. 2016; Camacho Villalón et al. 2019; Camacho Villalón et al. 2020; Swan et al. 2015; Lones 2020; Tzanetos and Dounias 2020) to put an end to the flood of increasingly outlandish, “novel” metaphor-centered metaheuristics, there is still a steady inflow of such papers submitted, and often accepted for publication. For many reasons, we believe this is detrimental to the field of metaheuristics. This letter describes some of the negative effects of publishing such papers in the literature and proposes a necessary action to try to put a limit to this highly undesirable phenomenon.

In this article, we rigorously analyze the intelligent water drops (IWD) algorithm, a metaphor-based approach for the approximate solution of discrete optimization problems proposed by Shah-Hosseini (in: Proceedings of the 2007 congress on evolutionary computation (CEC 2007), IEEE Press, Piscataway, NJ, pp 3226–3231, 2007). We demonstrate that all main algorithmic components of IWD are simplifications or special cases of ant colony optimization (ACO), and therefore, IWD is simply a particular instantiation of ACO. We show that the natural metaphor of “water drops flowing in rivers removing the soil from the riverbed”, the source of inspiration of IWD, is unnecessary, misleading and based on unconvincing assumptions of river dynamics and soil erosion that lack a real scientific rationale. We carry out a detailed review of modifications and extensions proposed to IWD since its first publication in 2007. We find that research on IWD is for the most part misguided and that the vast majority of the ideas explored in the literature on IWD have been studied many years before in the context of ACO. Finally, we discuss the use of natural metaphors as a source of inspiration for optimization algorithms, which has become an extremely popular trend in the last 15 years, and propose some criteria to limit their usage to the cases in which the metaphor is indeed useful., SCOPUS: ar.j, info:eu-repo/semantics/published