An improved artificial bee colony algorithm and its application to reliability optimization problems

Ghambari, Soheila; Rahati, Amin

doi:10.1016/j.asoc.2017.10.040

Cited by 66 publications

(20 citation statements)

References 86 publications

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“…A2 of the appendix are applied, and the dimensions of these test functions are all taken as 1000. Table 7 illustrates the parameter values of algorithms PSO, APSO, DE, SaDE, ABC and MABC that are obtained from references (Karaboga & Basturk 2008;Koombhongse et al 2008;Karaboga & Akay 2009;Zhang et al 2014;Ghambari & Rahati 2018). The parameters of FAT are the same as those of Section 4.1.…”

Section: Comparison Between Fat and Pso Apso De Sade Abc Mabcmentioning

confidence: 99%

The feedback artificial tree (FAT) algorithm

2020

Soft Comput

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Inspired by the transport of organic matters and the update theories of branches, the artificial tree (AT) algorithm was proposed recently. This work presents an improved version of AT algorithm that is called the feedback artificial tree (FAT) algorithm. In FAT, besides the transfer of organic matters, the feedback mechanism of moistures is introduced.Meanwhile, the self-propagating operator and dispersive propagation operator are also put forward. Some typical benchmark problems are applied to test the performance of FAT. The experimental results have clearly demonstrated the higher performance of FAT compared with AT over the tested set of problems. In addition, some well-known heuristic algorithms and their improved algorithms are also applied to validate the performance of FAT, and the computational results of FAT listed in this study are the best among these algorithms. In addition, sensitive analyses on the specific parameters of FAT algorithm are carried out, and the performance of FAT is validated.

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Section: Comparison Between Fat and Pso Apso De Sade Abc Mabcmentioning

confidence: 99%

The feedback artificial tree (FAT) algorithm

2020

Soft Comput

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“…e scout bee randomly selects a new food source and continues its work as an employed bee. e main parts of ABC algorithm are as follows [32]:…”

Section: Algorithms For Damage Detectionmentioning

confidence: 99%

A Novel Artificial Bee Colony Algorithm for Structural Damage Detection

Zhao

Yan

Yang

et al. 2020

Advances in Civil Engineering

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A novel artificial bee colony (ABC) algorithm to detect structural damage via modal and frequency analyses is proposed (named as TCABC algorithm). Compared to the standard ABC algorithm, tabu search method and chaotic search method are adopted in the proposed algorithm to enhance the exploration and exploitation ability. The tabu search method uses a memory function to avoid the solution being trapped in a local minimum, which increases the exploitation ability. Chaotic search method generates more searching points for finding the global minimum, which increases the exploration ability. Additionally, the first roulette wheel selection is replaced by the tournament selection to enhance the global searching ability of the TCABC algorithm. Several explicit test functions and an implicit damage detection function are employed to check the numerical results obtained from ABC and TCABC algorithms. Afterward, the damage detection accuracy of the TCABC algorithm is verified under different circumstances, and several recommendations are given for using the TCABC algorithm to detect structural damages under actual conditions. Finally, an experimental study is applied to examine the performance of TCABC algorithm for damage detection. The results show the following: (1) compared to traditional ABC algorithm, TCABC algorithm performs better; (2) fewer groups lead to faster convergence as demonstrated by both algorithms used in the same damage situation; (3) TCABC algorithm can infer the locations and extents of the damage when the groupings are inaccurate; (4) the accuracy of the field test data profoundly affects the precision of the damage detection results. In other words, stronger noises result in worse identification results; (5) whether or not the noises exist, the more data are measured, the more accurate the results can be achieved; (6) the TCABC algorithm can efficiently detect structural damage in the experimental study.

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“…Each onlooker bee selects one food source using a roulette wheel selection method. The probability of one food source being chosen is calculated by [26]:…”

Section: B Basic Abc Algorithmmentioning

confidence: 99%

“…Wang et al proposed multi-species ABC (MABC) algorithm and applicated in crowd simulation for evacuation [25]. However, the ABC algorithm does not perform well on every optimization problem due to certain limitations, including slow convergence rate and weak exploitation capability lacking effective guidance [26]. When the ABC algorithm is applied to the optimization procedure for pressure testing during riser reentry, an improved search strategy should consider the unique problem characteristics.…”

Section: Introductionmentioning

confidence: 99%

An Efficient Modeling and Optimization Approach for Pressure Testing During Reentry Operation of Deepwater Drilling Riser System

2020

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Pressure testing should be conducted on auxiliary lines during riser reentry to ensure the seal security of connection positions. The proper setting of the number and positions of pressure testing is essential because it can significantly reduce the high costs of offshore drilling. However, the analysis and optimization models for pressure testing during riser reentry were not resolved satisfactorily in engineering practice. This study proposes an efficient optimization procedure for pressure testing during riser reentry. A time-efficiency evaluation model and a risk assessment model are established based on the pressure testing process and entropy theory. The modified artificial bee colony (ABC) algorithm is used to optimize the pressure testing process by incorporating the above proposed models. The optimization procedure is illustrated by a case study. The optimal results from the proposed procedure are compared with those from the recommended practices. The resulting variabilities due to seal reliability, water depth, and pressure testing time are also discussed. The study shows that the optimization procedure can improve the operation efficiency of the riser reentry process by 20% and 38% compared to two recommended practices. The proposed procedure is verified to be an efficient alternative for decision making in pressure testing to improve the economic efficiency and ensure safety during riser reentry. INDEX TERMS Pressure testing, riser, time-efficiency evaluation, risk assessment, artificial bee colony algorithm, optimization. GUOMING CHEN received the B.E. and M.E. degrees in petroleum mechanical engineering and the Ph.D. degree in mechanical design and theory from the

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An improved artificial bee colony algorithm and its application to reliability optimization problems

Cited by 66 publications

References 86 publications

The feedback artificial tree (FAT) algorithm

The feedback artificial tree (FAT) algorithm

A Novel Artificial Bee Colony Algorithm for Structural Damage Detection

An Efficient Modeling and Optimization Approach for Pressure Testing During Reentry Operation of Deepwater Drilling Riser System

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