Construction Cost Minimization of Shallow Foundation Using Recent Swarm Intelligence Techniques
In this study, the performances of eight recent swarm intelligence techniques, accelerated particle swarm optimization (APSO), firefly algorithm, levy-flight krill herd, whale optimization algorithm (WOA), ant lion optimizer, grey wolf optimizer, moth-flame optimization algorithm and teaching-learni...
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| Veröffentlicht in: | IEEE transactions on industrial informatics 2018-03, Vol.14 (3), p.1099-1106 |
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| creator | Gandomi, Amir H. Kashani, Ali R. |
| description | In this study, the performances of eight recent swarm intelligence techniques, accelerated particle swarm optimization (APSO), firefly algorithm, levy-flight krill herd, whale optimization algorithm (WOA), ant lion optimizer, grey wolf optimizer, moth-flame optimization algorithm and teaching-learning-based optimization algorithm (TLBO), are explored. Particle swarm optimization algorithm is also considered to benchmark the efficiencies. A final cost is considered as an objective function which deals with shallow footing optimization with two attitudes: routine optimization, and sensitivity analysis. Moreover, as a further study, the effect of the location of the column at the top of the foundation is examined by adding two spare design variables. To this end, three numerical case studies are simulated. Based on the final results TLBO showed an acceptable performance because of the lowest mean values and WOA demonstrated the weakest efficiency among the algorithms in this study. |
| doi_str_mv | 10.1109/TII.2017.2776132 |
| format | Article |
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Particle swarm optimization algorithm is also considered to benchmark the efficiencies. A final cost is considered as an objective function which deals with shallow footing optimization with two attitudes: routine optimization, and sensitivity analysis. Moreover, as a further study, the effect of the location of the column at the top of the foundation is examined by adding two spare design variables. To this end, three numerical case studies are simulated. 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Particle swarm optimization algorithm is also considered to benchmark the efficiencies. A final cost is considered as an objective function which deals with shallow footing optimization with two attitudes: routine optimization, and sensitivity analysis. Moreover, as a further study, the effect of the location of the column at the top of the foundation is examined by adding two spare design variables. To this end, three numerical case studies are simulated. Based on the final results TLBO showed an acceptable performance because of the lowest mean values and WOA demonstrated the weakest efficiency among the algorithms in this study.</description><subject>Algorithm design and analysis</subject><subject>Algorithms</subject><subject>Bars</subject><subject>Computer simulation</subject><subject>Construction costs</subject><subject>Construction industry</subject><subject>global optimization</subject><subject>Heuristic methods</subject><subject>Informatics</subject><subject>Krill</subject><subject>Linear programming</subject><subject>Machine learning</subject><subject>Optimization</subject><subject>Optimization algorithms</subject><subject>Particle swarm optimization</subject><subject>Sensitivity analysis</subject><subject>shallow footing</subject><subject>Shallow foundations</subject><subject>Swarm intelligence</subject><subject>swarm intelligence techniques</subject><subject>Whales</subject><issn>1551-3203</issn><issn>1941-0050</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2018</creationdate><recordtype>article</recordtype><sourceid>RIE</sourceid><recordid>eNo9kFFLwzAUhYsoOKfvgi8BnzvvTZqmfZTitDAR3PZc2jTdMrpkJi1Df72dHT7dw-Gce-ALgnuEGSKkT6s8n1FAMaNCxMjoRTDBNMIQgMPloDnHkFFg18GN9zsAJoClk6DIrPGd62WnrSGZ9R1510bv9U_559iGLLdl29ojmdve1KO79tpsyKeSynRkeSzdnuSmU22rN8pIRVZKbo3-6pW_Da6asvXq7nynwXr-ssrewsXHa549L0JJU-zCKIGSYVqJmkslShRQlRLipq5qSmPESgCPE5AyiVnEWIQ0kUqmqWJNwptEsWnwOP49OHva7Yqd7Z0ZJouBCvCIc86GFIwp6az3TjXFwel96b4LhOKEsRgwngqiOGMcKg9jRSul_uMJYhynlP0CNGZusw</recordid><startdate>20180301</startdate><enddate>20180301</enddate><creator>Gandomi, Amir H.</creator><creator>Kashani, Ali R.</creator><general>IEEE</general><general>The Institute of Electrical and Electronics Engineers, Inc. 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| source | IEEE Electronic Library (IEL) |
| subjects | Algorithm design and analysis Algorithms Bars Computer simulation Construction costs Construction industry global optimization Heuristic methods Informatics Krill Linear programming Machine learning Optimization Optimization algorithms Particle swarm optimization Sensitivity analysis shallow footing Shallow foundations Swarm intelligence swarm intelligence techniques Whales |
| title | Construction Cost Minimization of Shallow Foundation Using Recent Swarm Intelligence Techniques |
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