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Bio-inspired optimization (including Evolutionary Computation and SwarmIntelligence) is a growing research topic with many competitive bio-inspiredalgorithms being proposed every year. In such an active area, preparing asuccessful proposal of a new bio-inspired algorithm is not an easy task. Giventhe maturity of this research field, proposing a new optimization techniquewith innovative elements is no longer enough. Apart from the novelty, resultsreported by the authors should be proven to achieve a significant advance overprevious outcomes from the state of the art. Unfortunately, not all newproposals deal with this requirement properly. Some of them fail to select anappropriate benchmark or reference algorithms to compare with. In other cases,the validation process carried out is not defined in a principled way (or iseven not done at all). Consequently, the significance of the results presentedin such studies cannot be guaranteed. In this work we review severalrecommendations in the literature and propose methodological guidelines toprepare a successful proposal, taking all these issues into account. We expectthese guidelines to be useful not only for authors, but also for reviewers andeditors along their assessment of new contributions to the field.

Fairness in Bio-inspired Optimization Research: A Prescription of Methodological Guidelines for Comparing Meta-heuristics