## 4 Discussion
Overall, the multi-faceted design of QSA--merging collision-based swarm behavior, quantum leaps, partial superposition, and local search--proved advantageous. Whether tackling continuous or discrete domains, the algorithm maintained a strong balance of exploration (through collisions and tunneling) and exploitation (through local search and best-solution-based superposition). Our experimental analysis on the 30-dimensional Levy benchmark demonstrates the clear statistical superiority of the proposed **QSA**. A one-way ANOVA applied to the seven competing meta-heuristics produced an \(F\)-value \(=704.59\) with \(p<1\times 10^{-15}\), unequivocally confirming overall performance differences. Follow-up pair-wise \(t\)-tests (\(\alpha=0.05\), unadjusted) further revealed that QSA delivers a significantly lower average error than every rival: **ABC**\((t=-22.79,\ p<1\times 10^{-15})\), **GA**\((t=-5.54,\ p=7.8\times 10^{-7})\), **PSO**\((t=-9.34,\ p=3.6\times 10^{-13})\), **SA**\((t=-43.87,\ p<1\times 10^{-15})\), **CS**\((t=-31.04,\ p<1\times 10^{-15})\), and even the quantum-inspired **QPSO**\((t=-2.10,\ p=0.040)\). Collectively, these statistics substantiate QSA's dominance in high-dimensional continuous optimisation and underscore its promise as a robust, state-of-the-art solver for complex search spaces. On the 30-dimensional _HyperSphere_ benchmark, the proposed QSA once more demonstrated decisive superiority. A one-way ANOVA conducted across the seven competing meta-heuristics produced an \(F=948.23\) with \(p\approx 1\times 10^{-16}\), leaving no doubt that the algorithms' mean errors differ substantially. Subsequent pair-wise two-sample \(t\)-tests (\(\alpha=0.05\), unadjusted) confirmed that QSA outperforms every rival by a comfortable statistical margin: **ABC**\((t=-29.88,\ p<10^{-15})\), **GA**\((t=-10.05,\ p\approx 2.6\times 10^{-14})\), **PSO**\((t=-3.57,\ p\approx 7.3\times 10^{-4})\), **SA**\((t=-36.25,\ p<10^{-15})\), **CS**\((t=-46.83,\ p<10^{-15})\),and even the quantum-inspired **QPSO**\((t=-7.55,\ p\approx 3.5\times 10^{-10})\). Collectively, these statistics underscore QSA's ability to deliver the lowest average error--often by more than an order of magnitude--thereby cementing its status as a robust, state-of-the-art solver for challenging high-dimensional optimisation landscapes. In metaheuristic optimization algorithms, two factors largely determine whether an algorithm will transition from a promising prototype to a deployable tool: parameter sensitivity and computational complexity. Parameter sensitivity describes how sharply performance responds to small changes in key hyper-parameters--such as population size, exploration-exploitation coefficients, or cooling schedules--which, if not robustly tuned, can lead to erratic convergence or premature stagnation across different problem instances. Computational complexity, encompassing both time and memory requirements, sets the practical ceiling for real-world adoption: an algorithm that delivers marginally better optima yet scales super-linearly in runtime or memory may be infeasible for high-dimensional or real-time applications. Discussing these dimensions in tandem enables fair benchmarking, highlights the trade-offs between accuracy and resource consumption, and guides practitioners in selecting parameter settings that balance solution quality with operational constraints.