Multi-Objective Dhole Optimization for Saturation-Aware Autonomous Underwater Forensic Search and Evidence Recovery Missions

Abstract

Abstract

Background: Underwater forensic investigations frequently involve searching, locating, and recovering submerged human remains and associated forensic evidence under challenging environmental conditions. Efficient coordination of multiple unmanned underwater vehicles (UUVs) can support forensic investigators by reducing search time, improving spatial coverage, and limiting unnecessary energy consumption during underwater recovery operations. This study proposes a Multi-Objective Dhole Optimization Algorithm (MO-DOA) for discrete saturation-aware allocation. Methods: The method maps each candidate to a continuous latent score matrix, decodes it to UUV-to-region assignments, repairs assignment and saturation violations, and uses Pareto dominance with a diversity-preserving external archive to select leaders for the Dhole Optimization Algorithm search, encircling, and attack phases. Results: Evaluation uses an 800 m × 500 m maritime benchmark with eight UUVs, four clustered target regions, and three inaccessible reef zones. In the supplied 20-run results package, MO-DOA attained HV = 0.921, IGD = 0.048, mission effectiveness = 93.8, energy/travel cost = 142.6, threat exposure = 0.220, saturation deviation = 0.50, and runtime = 10.7 s, outperforming CEA, Improved WOA, MDO, IMMP-GWO, and IPSO-VP on the reported criteria. Convergence curves show earlier improvement and sustained Pareto-front quality. Conclusion: The findings support MO-DOA as a compact Pareto-based allocation framework, subject to final verification of the supplied run-level statistics.