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Distributed bees algorithm parameters optimization for a cost efficient target allocation in swarms of robots.

Aleksandar Jevtić¹, Alvaro Gutiérrez

¹ETSI Telecomunicación, Universidad Politécnica de Madrid, Av. Complutense 30, 28040 Madrid, Spain. aleksandar.jevtic@upm.es

Sensors (Basel, Switzerland)

|February 21, 2012

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Optimizing the distributed bees algorithm (DBA) with a genetic algorithm enhances robot swarm deployment efficiency. This approach reduces travel distance for robot swarms, even with limited resources.

Area of Science:

Robotics and Swarm Intelligence
Distributed Systems
Optimization Algorithms

Background:

Large robot swarms offer advantages in exploring unknown environments and rapid area coverage.
Coordinating numerous robots, especially under resource constraints, presents significant challenges.
Effective target allocation is crucial for efficient swarm deployment.

Purpose of the Study:

To optimize the distributed bees algorithm (DBA) for improved target allocation in robot swarms.
To enhance deployment cost efficiency by optimizing DBA control parameters.
To investigate the trade-offs between cost efficiency and distribution error.

Main Methods:

Optimization of the distributed bees algorithm (DBA) using a genetic algorithm.

Keywords:

cooperative sensors distributed task allocation genetic algorithms multi-agent systems parameter optimization swarm robotics

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Application of the optimized DBA to distributed target allocation problems in robot swarms.

Experimental validation of the optimized algorithm's performance.

Main Results:

The optimized DBA significantly reduced deployment costs, measured by average robot travel distance.
Cost efficiency was achieved, though sometimes at the cost of increased distribution error.
The proposed approach demonstrated adaptability to scarce resource conditions.

Conclusions:

Parameter optimization of the DBA via genetic algorithms leads to more cost-efficient robot swarm deployments.
The optimized swarm intelligence approach effectively manages target allocation under limited resources.
The method provides a scalable solution for coordinating large robot teams in complex environments.