TL;DR
This paper investigates how different swarm communication network topologies affect the robustness and efficiency of algorithms like particle swarm optimization in hostile environments where agents may be lost.
Contribution
It provides a systematic study of the impact of agent loss on swarm performance across various network configurations, highlighting the advantages of small-world networks.
Findings
Small-world networks maximize performance under hostile conditions.
Agent loss significantly impacts communication topology effectiveness.
Trade-offs exist between efficiency, robustness, and network structure.
Abstract
Swarm Intelligence-based optimization techniques combine systematic exploration of the search space with information available from neighbors and rely strongly on communication among agents. These algorithms are typically employed to solve problems where the function landscape is not adequately known and there are multiple local optima that could result in premature convergence for other algorithms. Applications of such algorithms can be found in communication systems involving design of networks for efficient information dissemination to a target group, targeted drug-delivery where drug molecules search for the affected site before diffusing, and high-value target localization with a network of drones. In several of such applications, the agents face a hostile environment that can result in loss of agents during the search. Such a loss changes the communication topology of the agents…
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