Nature- and Bio-inspired Optimization: the Good, the Bad, the Ugly and the Hopeful

Nowadays, optimization has become an important issue for industrial systems and product development. From an engineering perspective, optimization implies adjusting or fine tuning the design of the system considering performance factors. Unfortunately, in many real-world problems there are no mathematical techniques capable of solving them within reasonable times. Consequently, optimization is done manually in many practical cases. Over the last decades many meta-heuristic optimization techniques have been inspired by natural phenomena and behavioral patterns observed in animals. As such, nature- and bio-inspired optimization allows optimizing a problem without requiring special knowledge about it, but only the fitness function to optimize and mechanisms to create possible solutions. Bio-inspired optimization techniques generate new candidate solutions intelligently in search for the best one for the problem. Although they do not guarantee to obtain the optimum solution, they can autonomously achieve good results within reasonable time, having been successfully used in manifold real-world problems. There are so many bio-inspired optimization proposals in the literature that it could be overwhelming to choose one. This research area is expanding every year, with more bio-inspired techniques, and tools devised to use them in real applications. However, not all bio-inspired solvers are interesting. Many proposals are mathematically similar to well-established meta-heuristics, so they could render equally similar results. Furthermore, the performance of many of them has not been rigorously confirmed by experimentation. This article elaborates on recent applications (the good), the lack of innovation of new metaphor-based algorithms (the bad), poor methodological practices (the ugly) and the exciting future of opportunities and challenges (the hopeful) of this research area. Nature- and bio-inspired optimization can be great alternatives to optimize complex processes in many real-world industrial problems. But above all, the use of bio-inspired solvers requires a global understanding on the current status of this area. Providing such an overarching view to the audience from a multi-faceted perspective is the ultimate purpose of this manuscript. Keywords: Bio-inspired optimization, Nature-inspired optimization, Optimization, Evolutionary Algorithm