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This paper presents a unified performance evaluation of eight swarm intelligence metaheuristic algorithms for Wireless Sensor Networks (WSNs) energy-aware clustering in a heterogeneous data environment. The study examines their performance in terms of optimization accuracy, computational cost, network lifetime, alive-node depletion dynamics, and residual energy behavior. A standardized simulation framework and non-parametric statistical testing are employed to ensure fair comparison. The results reveal diverse operating profiles: some algorithms display superior convergence quality, others achieve faster execution, while a subset demonstrates significantly longer energy sustainability. The complete performance trends and quantified differences provide practitioners with a comprehensive reference for selecting algorithms suited to WSN applications with heterogeneous data generation.
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