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# Experimental Characterization of Emergent Behavior in Bio-Inspired Swarm Intelligence Algorithms

## Details

**Authors** Lazo et al.

**Year** 2026

**Publisher** Biomimetics

**Discipline** Computer Science & AI

**Secondary disciplines** Complexity Science

[Read it at the publisher](https://doi.org/10.3390/biomimetics11080576) 
10.3390/biomimetics11080576

## In authors' words

### Abstract

Bio-inspired swarm metaheuristic algorithms constitute a widely used tool for solving complex optimization problems. However, the experimental characterization of their emergent behavior remains a methodological challenge. This study proposes an experimental framework for characterizing emergent behavior through swarm collective dynamics. The framework integrates complementary dynamic indicators and establishes relative diversity loss as a homogeneous criterion for defining equivalent comparison states across different search processes. The framework was evaluated using the Reptile Search Algorithm (RSA) and Draco Lizard Optimizer (DLO) as case studies, with Particle Swarm Optimization (PSO) serving as a reference algorithm. The results showed that swarm collective dynamics were associated with both the mathematical properties of the search landscape and the search mechanisms of each metaheuristic. Furthermore, relative diversity loss enabled the comparison of different metaheuristics within a common reference framework. In RSA, swarm reorganization occurred during the first iterations. DLO exhibited a more gradual evolution, whereas PSO showed an intermediate behavior between both dynamics. The proposed experimental framework provides a methodological basis for the experimental characterization of emergent behavior in swarm metaheuristics.

### What they found (results)

Comparing three swarm-intelligence optimization algorithms, the authors measured a new 'relative diversity loss' indicator and found each algorithm produced a characteristically different collective reorganization pattern (rapid, gradual, or intermediate) that was not predictable from individual-agent search rules alone.

## Commentary

### In short

Whole-swarm reorganization dynamics are a real, measurable level of description distinct from and not reducible to the individual-agent rules that generate them.

**Patterns it shows** S

**Added** 2026-08-14

**How to cite this** Lazo et al. (2026). Experimental Characterization of Emergent Behavior in Bio-Inspired Swarm Intelligence Algorithms. Biomimetics.