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An Adaptive Tunicate Swarm Algorithm for Optimization of Shallow Foundation
This paper aims to introduce an adaptive metaheuristic algorithm based on tunicate swarm optimization (TSA) for effectively solving global optimization problems and the optimum design of a shallow spread foundation. The proposed adaptive tunicate swarm optimization (ATSA) has two main phases at each...
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| Published in: | IEEE access 2022, Vol.10, p.39204-39219 |
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| creator | Arabali, Amirbahador Khajehzadeh, Mohammad Keawsawasvong, Suraparb Mohammed, Adil Hussein Khan, Baseem |
| description | This paper aims to introduce an adaptive metaheuristic algorithm based on tunicate swarm optimization (TSA) for effectively solving global optimization problems and the optimum design of a shallow spread foundation. The proposed adaptive tunicate swarm optimization (ATSA) has two main phases at each iteration: searching all around the search space based on a randomly selected tunicate and improving the search using the position of the best tunicate. This modification improves the algorithm's exploration ability while also preventing premature convergence. The suggested algorithm's performance is confirmed using a set of 23 mathematical test functions of well-known CEC 2017 and the outcomes are compared with TSA as well as some effective optimization algorithms. In addition, the new method automates the optimum design of shallow spread foundations while taking two objectives into account: cost and CO 2 emissions. The analysis and design procedures are based on both geotechnical and structural limit states. A case study of a spread foundation has been solved using the proposed methodology, and a sensitivity analysis has been conducted to investigate the effect of soil parameters on the total cost and embedded CO 2 emissions of the foundation. The simulation results demonstrate that, when compared to other competing algorithms, ATSA is superior and may produce better optimal solutions. |
| doi_str_mv | 10.1109/ACCESS.2022.3164734 |
| format | article |
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The proposed adaptive tunicate swarm optimization (ATSA) has two main phases at each iteration: searching all around the search space based on a randomly selected tunicate and improving the search using the position of the best tunicate. This modification improves the algorithm's exploration ability while also preventing premature convergence. The suggested algorithm's performance is confirmed using a set of 23 mathematical test functions of well-known CEC 2017 and the outcomes are compared with TSA as well as some effective optimization algorithms. In addition, the new method automates the optimum design of shallow spread foundations while taking two objectives into account: cost and CO 2 emissions. The analysis and design procedures are based on both geotechnical and structural limit states. A case study of a spread foundation has been solved using the proposed methodology, and a sensitivity analysis has been conducted to investigate the effect of soil parameters on the total cost and embedded CO 2 emissions of the foundation. The simulation results demonstrate that, when compared to other competing algorithms, ATSA is superior and may produce better optimal solutions.</description><identifier>ISSN: 2169-3536</identifier><identifier>EISSN: 2169-3536</identifier><identifier>DOI: 10.1109/ACCESS.2022.3164734</identifier><identifier>CODEN: IAECCG</identifier><language>eng</language><publisher>Piscataway: IEEE</publisher><subject>Adaptive algorithms ; Algorithms ; Carbon dioxide ; Concrete ; cost ; Cost analysis ; Costs ; CO₂ emissions ; Design optimization ; Functions (mathematics) ; Global optimization ; Heuristic algorithms ; Heuristic methods ; Iterative methods ; Limit states ; Linear programming ; Mathematical analysis ; metaheuristic ; Metaheuristics ; Optimization ; Particle swarm optimization ; Sensitivity analysis ; shallow foundation ; Shallow foundations ; Soil investigations ; Spread foundations ; Tunicate swarm</subject><ispartof>IEEE access, 2022, Vol.10, p.39204-39219</ispartof><rights>Copyright The Institute of Electrical and Electronics Engineers, Inc. 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| subjects | Adaptive algorithms Algorithms Carbon dioxide Concrete cost Cost analysis Costs CO₂ emissions Design optimization Functions (mathematics) Global optimization Heuristic algorithms Heuristic methods Iterative methods Limit states Linear programming Mathematical analysis metaheuristic Metaheuristics Optimization Particle swarm optimization Sensitivity analysis shallow foundation Shallow foundations Soil investigations Spread foundations Tunicate swarm |
| title | An Adaptive Tunicate Swarm Algorithm for Optimization of Shallow Foundation |
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