Comparing swarm algorithms for multi-source localization

This paper proposes a common set of validation benchmarks and a reference algorithm that provide ground-truth for comparative analysis of swarming algorithms for multi-source localization. The benchmarks capture the primary first-order attributes of the general problem: source characterization and distribution, initial robot distributions, and dead space. The Biased Random Walk (BRW) reference algorithm represents a simple approach without robot communication. We demonstrate how the benchmarks are used, in combination with sensitivity analysis, to provide insights into the relative performance of algorithms. The reproduced Glowworm Swarm Optimization (GSO) algorithm and a new GSO/BRW hybrid algorithm are evaluated in an attempt to improve upon the baseline BRW performance. Unfortunately, none of the algorithms presented are able to guarantee localization of all sources on all benchmark cases, and their convergence properties differ considerably.