A discrete hybrid algorithm based on Differential Evolution and Cuckoo Search for optimizing the layout of ship pipe route

Lin, Yan; Bian, Xuanyi; Dong, Zongran

doi:10.1016/j.oceaneng.2022.112164

Cited by 18 publications

(9 citation statements)

References 20 publications

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“…By maximizing the usage of piping support, costs were reduced by 5.59%, and the length of the piping route was shortened by 13.25%. Lin et al [69] suggested differential evolution and cuckoo search (DDECS) for the problem of ship pipe route layout optimization. Tsai et al [70] developed a site pipeline inspection and auto-adjusting approach by utilizing augmented reality and a grid-based path planning algorithm.…”

Section: Auto-routing System For Engineering Designmentioning

confidence: 99%

Auto-Routing Systems (ARSs) with 3D Piping for Sustainable Plant Projects Based on Artificial Intelligence (AI) and Digitalization of 2D Drawings and Specifications

Kang,

Choi,

Lee

et al. 2024

Sustainability

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The engineering sector is undergoing digital transformation (DT) alongside shifts in labor patterns. This study concentrates on piping design within plant engineering, aiming to develop a system for optimal piping route design using artificial intelligence (AI) technology. The objective is to overcome limitations related to time and costs in traditional manual piping design processes. The ultimate aim is to contribute to the digitalization of engineering processes and improve project performance. Initially, digital image processing was utilized to digitize piping and instrument diagram (P&ID) data and establish a line topology set (LTS). Subsequently, three-dimensional (3D) modeling digital tools were employed to create a user-friendly system environment that visually represents piping information. Dijkstra’s algorithm was implemented to determine the optimal piping route, considering various priorities during the design process. Finally, an interference avoidance algorithm was used to prevent clashes among piping, equipment, and structures. Hence, an auto-routing system (ARS), equipped with a logical algorithm and 3D environment for optimal piping design, was developed. To evaluate the effectiveness of the proposed model, a comparison was made between the bill of materials (BoM) from Company D’s chemical plant project and the BoM extracted from the ARS. The performance evaluation revealed that the accuracy in matching pipe weight and length was 105.7% and 84.9%, respectively. Additionally, the accuracy in matching the weight and quantity of fittings was found to be 99.7% and 83.9%, respectively. These findings indicate that current digitalized design technology does not ensure 100% accurate designs. Nevertheless, the results can still serve as a valuable reference for attaining optimal piping design. This study’s outcomes are anticipated to enhance work efficiency through DT in the engineering piping design sector and contribute to the sustainable growth of companies.

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Section: Auto-routing System For Engineering Designmentioning

confidence: 99%

Auto-Routing Systems (ARSs) with 3D Piping for Sustainable Plant Projects Based on Artificial Intelligence (AI) and Digitalization of 2D Drawings and Specifications

Kang,

Choi,

Lee

et al. 2024

Sustainability

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“…Abraham [42] combined the firefly algorithm (FA) with the DE algorithm. Lin et al [43] combined the cuckoo search algorithm with the DE algorithm. DE algorithm has also been combined with other algorithms such as the whale algorithm [43], frog-leaping algorithm [44], grey wolf optimizer [46], and bat algorithm [47].…”

Section: Combination With Other Algorithmsmentioning

confidence: 99%

“…Lin et al [43] combined the cuckoo search algorithm with the DE algorithm. DE algorithm has also been combined with other algorithms such as the whale algorithm [43], frog-leaping algorithm [44], grey wolf optimizer [46], and bat algorithm [47]. In addition to combining with other algorithms, the DE algorithm can also be combined with improved variants of the DE algorithm itself.…”

Section: Combination With Other Algorithmsmentioning

confidence: 99%

Multi-population multi-strategy differential evolution algorithm with dynamic population size adjustment

Xue,

Liu,

Deng

et al. 2023

Preprint

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Differential Evolution (DE) algorithm is a type of global optimization algorithm based on population searching for the optimal solution. It has characteristics such as fast convergence speed, simple and understandable algorithm, few parameters, and high stability. To further enhance its exhibition, we propose a differential evolution algorithm based on subpopulation adaptive scale and multi-adjustment strategy (ASMSDE). The algorithm divides population into three sub-populations based on the fitness values, the different operation strategies are adopted according to their characteristics. The superior population and inferior population adopt respectively Gaussian disturbance and Levy flights. The task of the intermediate population is to maintain the overall diversity of the population. The sizes of the three sub-populations are adaptively adjusted based on the evolutionary results to accommodate changes in individual differences during the evolution process. With the number of iterations increases and the differences between individuals decrease, adopt single population model instead of multi-population model in the later stage of evolution. To evaluate the performance of the ASMSDE algorithm, it is compared with other advanced algorithms using benchmark function optimizations. Experimental results demonstrate that the ASMSDE algorithm outperforms the comparison algorithms in most cases, validating its effectiveness and ability to handle local optima situations.

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“…The disadvantage of this algorithm is that the efficiency and quality of the pipeline arrangement decrease to different degrees when the population size increases. Lin et al [23] studied the ship piping arrangement problem based on the cuckoo search algorithm and proposed a more compatible and gridindependent coding method, accelerating the piping arrangement efficiency. However, the arrangement of parallel piping still needs to be studied.…”

Section: Introductionmentioning

confidence: 99%

The Intelligent Layout of the Ship Piping System Based on the Optimization Algorithm

Wei,

Wu,

et al. 2024

Applied Sciences

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The ship piping layout is one of the essential tasks in the detailed design stage of a ship. Traditional manual expert design has disadvantages such as low efficiency, reliance on experience, and subjective influence. Therefore, this paper systematically proposes an intelligent arrangement method for ships’ single, parallel, and branch pipelines. Firstly, the traditional genetic algorithm is improved and combined with the A* algorithm to solve the intelligent arrangement problem of a single pipeline in ships. Then, the parallel pipeline and branch pipeline are split into multiple single pipelines by combining with the connection point strategy to solve the arrangement problem of parallel pipeline and branch pipeline. Finally, the optimized A*-genetic algorithm proposed in this paper is compared with the A* algorithm, particle swarm algorithm, and the labyrinth-genetic algorithm used in previous research through simulation experiments. The results show that the A*-genetic algorithm of this paper is optimal in six indexes, including length, number of elbows, energy value, fitness value, number of optimal solutions, and average number of convergence generations, in the arrangement of the single pipeline. In solving the parallel pipeline and branch pipeline arrangement problems, the all-around performance of this paper’s algorithm is better than that of A*-genetic algorithm and maze–genetic algorithm, respectively. The A*-genetic algorithm of this paper considers the quality of pipeline arrangement and the solution’s efficiency. It verifies the adaptability and superiority of the algorithm for the intelligent arrangement of various types of pipelines in ship pipelines.

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A discrete hybrid algorithm based on Differential Evolution and Cuckoo Search for optimizing the layout of ship pipe route

Cited by 18 publications

References 20 publications

Auto-Routing Systems (ARSs) with 3D Piping for Sustainable Plant Projects Based on Artificial Intelligence (AI) and Digitalization of 2D Drawings and Specifications

Auto-Routing Systems (ARSs) with 3D Piping for Sustainable Plant Projects Based on Artificial Intelligence (AI) and Digitalization of 2D Drawings and Specifications

Multi-population multi-strategy differential evolution algorithm with dynamic population size adjustment

The Intelligent Layout of the Ship Piping System Based on the Optimization Algorithm

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