Sunn Pest Optimization (SPO): A Phase-Differentiated Bio-Inspired Metaheuristic for Global Optimization with Application to Precision Agriculture Monitoring Networks

Metaheuristic optimization increasingly requires search operators with functionally justified biological grounding rather than superficial metaphors. This paper introduces Sunn Pest Optimization (SPO), a novel algorithm derived from the bioecology of the cereal pest Eurygaster integriceps. SPO couples directional Lévy flight exploration with differential swarm exploitation while utilizing a tiered stagnation hierarchy and a spatial diversity-override mechanism to manage population density. The algorithm was evaluated against seven classical and modern baselines across 16 CEC 2017 and CEC 2022 benchmark functions. At ten dimensions, SPO achieved the second-best average rank behind the state-of-the-art L-SHADE, outperforming WOA, GWO, and GBFIO in corrected pairwise comparisons while showing a mixed record against GA, PSO, and DE. Scalability tests at thirty dimensions showed that SPO retains its edge over several swarm-based methods but loses parity with classical evolutionary approaches. To assess practical readiness, the algorithm was applied to a budget-limited precision agriculture task involving the spatial placement of pest scouting stations. Although SPO achieved lower numerical coverage than the top performers, it reliably satisfied logistical constraints and successfully concentrated monitoring resources in high-risk zones to support integrated pest management. SPO is a transparent, biologically coherent metaheuristic with clearly defined scalability boundaries and demonstrated applicability to constrained real-world spatial design.