Survival of the flexible: explaining the recent dominance of nature-inspired optimization within a rapidly evolving world

TL;DR

This paper provides evidence that nature-inspired meta-heuristics are surpassing traditional optimization methods in research and commercial activity, attributing their success to flexibility and hybridization tailored to diverse problem environments.

Contribution

It offers a comprehensive explanation for NIM's popularity, emphasizing the role of flexibility and hybridization in their success across evolving problem landscapes.

Findings

NIM use has surpassed MOT in research publications and patents.

Flexibility and hybridization are key to NIM's success.

Global trends favor flexible optimization frameworks.

Abstract

Although researchers often comment on the rising popularity of nature-inspired meta-heuristics (NIM), there has been a paucity of data to directly support the claim that NIM are growing in prominence compared to other optimization techniques. This study presents evidence that the use of NIM is not only growing, but indeed appears to have surpassed mathematical optimization techniques (MOT) in several important metrics related to academic research activity (publication frequency) and commercial activity (patenting frequency). Motivated by these findings, this article discusses some of the possible origins of this growing popularity. I review different explanations for NIM popularity and discuss why some of these arguments remain unsatisfying. I argue that a compelling and comprehensive explanation should directly account for the manner in which most NIM success has actually been achieved, e.g. through hybridization and customization to different problem environments. By taking a problem lifecycle perspective, this paper offers a fresh look at the hypothesis that nature-inspired meta-heuristics derive much of their utility from being flexible. I discuss global trends within the business environments where optimization algorithms are applied and I speculate that highly flexible algorithm frameworks could become increasingly popular within our diverse and rapidly changing world.