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particle bee algorithm for tower crane layout with material quantity supply and demand optimization

AUTOMATION IN CONSTRUCTION 2014年第Sep.期45卷 25-32页

作者： Lien, Li-Chuan Cheng, Min-Yuan Fujian Univ Technology Coll Civil Engn Fujian 350108 Peoples R China Natl Taiwan Univ Sci & Technol Dept Construct Engn Taipei 106 Taiwan

The tower crane layout (TCL) problem, a typical construction site layout (CSL) problem, is currently used in a wide range of construction projects and site conditions. The tower crane is a key facility used in the vertical and horizontal transportation of materials, particularly heavy prefabrication units such as steel beams, ready-mixed concrete, prefabricated elements, and large-panel formwork. Matching the location of tower cranes to material supply and engineering demands is a combinatorial optimization issue within the TCL problem that is difficult to resolve. Swarm intelligence (SI) is a popular artificial intelligence technique that is used widely to resolve complex optimization problems. Various SI-based algorithms have been developed that emulate the collective behavior of animals such as honey bees (bee algorithm, BA) and birds (particle swarm optimization, PSO). This study applies the particle bee algorithm (PBA), a hybrid swarm algorithm that integrates the respective advantages of honey bee and bird swarms, to the TCL problem. The performances of PBA. BA, and PSO are compared in terms of their effectiveness in resolving a practical TCL problem in construction engineering. Results show that the PBA performs better than both the BA and PSO algorithms. (C) 2014 Elsevier B.V. All rights reserved.

关键词： Tower crane layout Swarm intelligence bee algorithm particle swarm optimization particle bee algorithm

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BIM-based steel reinforcing bar detail construction design and picking optimization

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STRUCTURES 2023年 49卷 520-536页

作者： Lien, Li-Chuan Dolgorsuren, Unurjargal Chung Yuan Christian Univ Dept Civil Engn Taoyuan 320 Taiwan

Steel reinforcing bar (SRB) engineering is a very professional, complex, massive, and neglected project, which affects the structural performance and cost of building. Traditional SRB engineering is a labor-intensive project, often accompanied by the quality of SRB construction and the waste of material. At present, information tech-nology is undergoing a revolution. With the rise and optimization of Building Information Modeling (BIM) technology and the continuous improvement of artificial intelligence-based optimization algorithms, new solu-tions have been put forward to solve these traditional SRB engineering problems. This study combines two different fields of BIM and optimization algorithms to establish a process of BIM-based SRB detail construction design and picking optimization that provides a reference for traditional SRB engineering. It is also an explo-ration and application of BIM technology to practical engineering. This study classified and analyzed the SRB crash nodes problems classified into four crash nodes classification and proposed a BIM-based SRB designing process. For the problem of the SRB connection position, a general segmental formula for SRB (for example, Longitudinal SRB on columns and beams) was studied, and a set of processes was established to determine the unknowns of the segment formula to locate the connection position of SRB. Besides, this study utilizes BIM's coherent information transfer and visualization technologies that select Revit software to create new information such as "SRB shape family," "component family," "annotated family," and so on. The application of BIM in SRB is carried out through the transfer, transformation, and expression of information in the forms of SRB design drawings, SRB processing schedules, SRB construction schedules, and SRB construction partition charts, and so on. in addition, the problem of one-dimensional blanking of single-size and multiple-size raw materials is discussed with the minimum numb

关键词： Building information modeling Detail construction design Picking optimization Optimization algorithm particle bee algorithm

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A hybrid AI-based particle bee algorithm for facility layout optimization

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ENGINEERING WITH COMPUTERS 2012年第1期28卷 57-69页

作者： Cheng, Min-Yuan Lien, Li-Chuan Natl Taiwan Univ Sci & Technol Dept Construct Engn Taipei 106 Taiwan

Facility layout (FL) design presents a particularly interesting area of study because of its relatively high level of attention to aesthetics and usability qualities, in addition to common engineering objectives such as cost and performance. However, this generally presents a difficult combinatorial optimization problem for engineers. Swarm intelligence, an approach to decision making that integrates collective social behavior models such as the bee algorithm (BA) and particle swarm optimization (PSO), is being increasingly used to resolve various complex optimization problems. In order to integrate BA global search ability with the local search advantages of PSO, this study proposes a new optimization hybrid swarm algorithm-the particle bee algorithm (PBA) which imitates the intelligent swarming behavior of honeybees and birds. This study also proposes a neighborhood-windows technique for improving searching efficiency as well as a self-parameter-updating technique for preventing trapping into a local optimum in high-dimensional problems. This study compares PBA performance against BA and PSO performance in practical FL problem. Results show PBA performance is comparable to those of BA and PSO and can be efficiently employed to solve practical FL problem with high dimensionality.

关键词： Facility layout Swarm intelligence bee algorithm particle swarm optimization particle bee algorithm

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Hybrid Artificial Intelligence-Based PBA for Benchmark Functions and Facility Layout Design Optimization

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JOURNAL OF COMPUTING IN CIVIL ENGINEERING 2012年第5期26卷 612-624页

作者： Cheng, Min-Yuan Lien, Li-Chuan Natl Taiwan Univ Sci & Technol Dept Construct Engn Taipei 106 Taiwan

Facility layout design (FLD) presents a particularly interesting area of study because of its relatively high level of attention to aesthetics and usability qualities, in addition to common engineering objectives such as cost and performance. However, FLD present a difficult combinatorial optimization problem for engineers. Swarm intelligence (SI), an approach to decision making that integrates collective social behavior models such as the bee algorithm (BA) and particle swarm optimization, is being increasingly used to resolve various complex optimization problems. This study proposes a new optimization hybrid swarm algorithm, the particle bee algorithm (PBA), which imitates the intelligent swarming behavior of honeybees and birds. This study also proposes a neighborhood-windows (NW) technique for improving searching efficiency and a self-parameter-updating (SPU) technique for preventing trapping into a local optimum in high-dimensional problems. This study compares the performance of PBA with that of genetic algorithm (GA), differential evolution (DE), bee algorithm, and particle swarm optimization for multidimensional benchmark function problems. Additionally, this study compares PBA performance against bee algorithm and particle swarm optimization (PSO) performance in practical FLD problems. Results show that PBA performance is comparable to those of the mentioned algorithms in the benchmark functions and can be efficiently employed to solve practical FLD problem with high dimensionality. DOI: 10.1061/(ASCE)CP.1943-5487.0000163. (C) 2012 American Society of Civil Engineers.

关键词： Facility layout design Swarm intelligence bee algorithm particle swarm optimization particle bee algorithm Benchmark function

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