Scuba Diver Optimization Algorithm: An oxygen-state-regulated metaheuristic with integrated validation on constrained engineering and mathematical benchmarks

This paper introduces the Scuba Diver Optimization Algorithm (SDOA), a state-regulated population-based metaheuristic for constrained and continuous optimization. The main contribution is an agent-level oxygen-state regulator that jointly controls search stage, movement operator, perturbation amplitude, local refinement, elite communication, and reset pressure. In contrast to iteration-level schedules that impose a single exploration-to-exploitation trajectory on the entire population, SDOA allows candidate solutions to occupy different behavioral stages within the same iteration. This asynchronous state allocation is intended to support global coverage, boundary-sensitive exploitation, and diversity recovery through one search-governance mechanism. The empirical evaluation uses an integrated validation corpus comprising four constrained engineering design problems–welded beam design, tension/compression spring design, car side-impact design, and speed reducer design–and twenty-three standard mathematical functions representing unimodal, multimodal, discontinuous, penalized, and fixed-dimensional landscapes. The experimental protocol reports benchmark formulations, parameter settings, sensitivity evidence, repeated-run dispersion where available, confidence intervals, runtime diagnostics, and mechanism-level interpretation. The findings are therefore interpreted in terms of feasibility, robustness, computational cost, and comparative standing, rather than isolated best-run values. Overall, SDOA is positioned as a transparent state-regulated optimizer whose principal contribution is the coordinated regulation of feasibility recovery, exploitation control, and diversity preservation within a coherent internal state model.