Disorder-Oriented Genetic Algorithm for Exploration Optimisation in Robot Swarms

• Gabriel A. C. Batista UFU
• Eduardo S. Rocha UFU
• Claudiney R. Tinoco UFU

DOI: https://doi.org/10.5753/eniac.2026.31480

Resumo

Parameter selection is a critical aspect of robot swarm coordination, as it directly affects collective behaviour and task performance. However, manually determining these parameters becomes increasingly difficult as environmental complexity and task requirements increase. In this context, this work proposes the application and adaptation of an evolutionary algorithm to optimize the parameters of the PheroCom model for swarm coordination. To achieve this, entropy is incorporated as an evaluation metric in the fitness function, acting as a multiplicative factor within a penalty scheme during task execution. This allows the identification of parameter settings that promote a more balanced spatial distribution of robots, improving environmental coverage while avoiding both excessive concentration and neglected regions. Experimental results show that incorporating entropy into the evaluation criterion provides an effective measure of exploration uniformity and leads to a more consistent distribution of robots throughout the environment. In addition, the optimized model achieves competitive performance compared with the reference configuration.

Palavras-chave: Swarm Robotics, Genetic Algorithms, Parameter Optimization, Entropy, Environment Coverage, Bio-inspired Algorithms

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