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Multi-objective 3D piping layout optimization for offshore platforms using improved IABC-ACO algorithm
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DOI：10.1016/j.oceaneng.2026.127469.png)
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Abstract
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Piping routing design is critical for the integrated construction of ships and offshore platforms, directly dictating operational efficiency, construction costs, and pressure loss. To address the multi-objective 3D piping layout problem constrained by elbow count, path smoothness, and pressure loss, this study proposes a novel Improved Artificial Bee Colony-Ant Colony Optimization (IABC-ACO) algorithm. Specifically, standard ABC performance is enhanced through three targeted strategies: a chaotic initialization with a mutual exclusion mechanism to diversify the initial population distribution; a global-optimum-guided neighborhood search utilizing an adaptive compression factor to systematically balance global exploration and local exploitation; and a continuous bee strategy to prevent the premature loss of valid solutions. Concurrently, direction- and turning-penalty heuristic functions are integrated into the ACO to guide ant path selection and accelerate search times, while dedicated orthogonalization and de-folding operations are embedded to ensure geometric rationality. Comparative benchmark experiments demonstrate that the proposed IABC-ACO achieves a minimum elbow count of 4, outperforming standard ABC, ACO, Genetic Algorithm (GA), and Particle Swarm Optimization (PSO) by 20% to 50%. Furthermore, it yields a superior average fitness value and accelerates convergence to just 18 generations, demonstrating enhanced optimization stability. Ultimately, its successful application to a practical offshore platform processing module validates that the algorithm generates a compact, orthogonal layout with minimized pressure loss, offering robust technical support for the intelligent automated design of offshore piping systems.
Keywords:
Piping layout optimization
Artificial bee colony algorithm
Ant colony optimization
Offshore platform
Multi-objective optimization
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