Hybrid GA-PSO Model for Multi-objective Dynamic Optimization of Cruise Cabin Layouts under Marine Operational Constraints (Informatica, 2026)
Cruise cabin space design stands as a pivotal determinant of passenger experience and operational efficiency, yet it grapples with three core challenges: irregular spatial configurations shaped by hull curvature, dynamic demands (e.g., fluctuating passenger load, real-time activity needs), and inherent multi-objective conflicts (e.g., maximizing space utilization vs. ensuring comfort). Traditional optimization methods—such as standalone heuristic algorithms or static layout planning—are constrained by limited global search capabilities, sluggish convergence in complex scenarios, and inability to adapt to maritime dynamic constraints. To address these gaps, this study innovatively proposes a hybrid GA-PSO bimodal algorithm and constructs a dynamic cruise cabin space optimization model. Marine-specific dynamic constraints are explicitly modeled, including ship motion parameters (roll/pitch angles under sea conditions 3–8) and maritime evacuation regulations (minimum channel width ≥1.2m). Four mutually restrictive objective functions are also formalized: space utilization (ratio of effective area to total area), comfort (integrated score of ergonomic spacing/noise isolation), structural feasibility (compatibility with hull load-bearing limits), and life-cycle cost (construction + maintenance expenses).Experiments utilize 12 typical cabin layouts (200–300㎡), 500+ passenger behavior datasets (18 months of real-ship data), and 30 independent runs, comparing with standard GA and PSO. Results verify superiority: GA-PSO convergence speed reaches 0.80 ± 0.05 iterations/s (mean ± std), which is 38.7% faster than GA (0.58 ± 0.08 iter/s) and 22.1% faster than PSO (0.66 ± 0.07 iter/s). The improvement is statistically significant (p < 0.01, t-test).; optimal solution comprehensive value hits 0.92 (15.2% higher than GA, 9.8% than PSO), with statistical significance (p