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 and hence
the information available to agents, ultimately influencing the performance of
the algorithm. In this paper, we present a study of the impact of loss of
agents on the performance of such algorithms as a function of the initial
network configuration. We use particle swarm optimization to optimize an
objective function with multiple sub-optimal regions in a hostile environment
and study its performance for a range of network topologies with loss of
agents. The results reveal interesting trade-offs between efficiency,
robustness, and performance for different topologies that are subsequently
leveraged to discover general properties of networks that maximize performance.
Moreover, networks with small-world properties are seen to maximize performance
under hostile conditions