Comprehensive Taxonomies of Nature- and Bio-inspired Optimization: Inspiration versus Algorithmic Behavior, Critical Analysis and Recommendations (from 2020 to 2024)

TL;DR

Nature- and bio-inspired optimization has exploded in volume, prompting a two-pronged taxonomy—by inspiration source and by search behavior—to organize hundreds of proposals. The study finds a weak link between the biological metaphor and the actual algorithmic behavior, with a large share of solvers acting as incremental variants of classic methods like PSO, DE, and GA. It also highlights serious methodological issues, including reproducibility gaps and unfair benchmarking, and offers actionable recommendations to improve practices and comparability. The work argues for focusing on algorithmic behavior and practical performance, while recognizing the potential of population-based optimization within AI ecosystems and GPAIS to shape future AI systems.

Abstract

In recent years, bio-inspired optimization methods, which mimic biological processes to solve complex problems, have gained popularity in recent literature. The proliferation of proposals prove the growing interest in this field. The increase in nature- and bio-inspired algorithms, applications, and guidelines highlights growing interest in this field. However, the exponential rise in the number of bio-inspired algorithms poses a challenge to the future trajectory of this research domain. Along the five versions of this document, the number of approaches grows incessantly, and where having a new biological description takes precedence over real problem-solving. This document presents two comprehensive taxonomies. One based on principles of biological similarity, and the other one based on operational aspects associated with the iteration of population models that initially have a biological inspiration. Therefore, these taxonomies enable researchers to categorize existing algorithmic developments into well-defined classes, considering two criteria: the source of inspiration, and the behavior exhibited by each algorithm. Using these taxonomies, we classify 518 algorithms based on nature-inspired and bio-inspired principles. Each algorithm within these categories is thoroughly examined, allowing for a critical synthesis of design trends and similarities, and identifying the most analogous classical algorithm for each proposal. From our analysis, we conclude that a poor relationship is often found between the natural inspiration of an algorithm and its behavior. Furthermore, similarities in terms of behavior between different algorithms are greater than what is claimed in their public disclosure: specifically, we show that more than one-fourth of the reviewed solvers are versions of classical algorithms. The conclusions from the analysis of the algorithms lead to several learned lessons.

Figures (9)

• Figure 1: Number of papers with bio-inspired optimization and nature-inspired optimization in the title, abstract and/or keywords, over the period 2005–April 2024 (Scopus database).
• Figure 2: Classification of the reviewed papers using the inspiration source based taxonomy.
• Figure 3: Ratio of reviewed algorithms by its category (first taxonomy).
• Figure 4: Schematic diagrams of the different algorithmic behaviors on which our second taxonomy relies. The upper plots illustrate the process of generating new solutions by Differential Vector Movement from a given solution $\mathbf{x}_A$, using (a) the entire population; (b) relevant individuals (in the example, the movement results from a weighted combination -- $\omega$-- of the current best solution in the population and the best solution found so far by the algorithm); and (c) neighboring solutions in the population to the reference individual. The lower plots show the same process using solution creation by (d) combination; and (e) stigmergy.
• Figure 5: Classification of the reviewed papers using the behavior taxonomy.