A platform for research: civil engineering, architecture and urbanism
A platform for research: civil engineering, architecture and urbanism
An Enhanced Symbiotic Organism Search Algorithm (ESOS) for the Sizing Design of Pin Connected Structures
https://orcid.org/http://orcid.org/0000-0002-5502-059X
The symbiotic organism search (SOS) algorithm is a strong metaheuristic search engine with a great explorative capability which enables it to search for global optima efficiently. Nevertheless, its exploitive ability is less profound leading to a relatively low rate of convergence. In the current paper, an enhanced symbiotic organism search (ESOS) algorithm with the aim of improving the exploitive capability of the SOS algorithm is presented. The enhancements are done in the commensalism and mutualism phases of the algorithm. In the current study, a multi-ecosystem mechanism is also employed. The presented ESOS algorithm is used along with two different constraint handling methods, namely mapping strategy (MS) and penalty function (PF). This leads to two variants of the ESOS, termed as ESOS-MS and ESOS-PF. The two presented ESOS variants are utilized to optimize the weight of four benchmark truss structures. The results are compared with the optimal designs reported in the literature. The results demonstrate that the proposed ESOS variants not only can find better solutions compared to the other algorithms, but also are faster in doing so. Moreover, of the two presented ESOS variants, the ESOS-MS variant performs much better than the ESOS-PF variant.
An Enhanced Symbiotic Organism Search Algorithm (ESOS) for the Sizing Design of Pin Connected Structures
https://orcid.org/http://orcid.org/0000-0002-5502-059X
The symbiotic organism search (SOS) algorithm is a strong metaheuristic search engine with a great explorative capability which enables it to search for global optima efficiently. Nevertheless, its exploitive ability is less profound leading to a relatively low rate of convergence. In the current paper, an enhanced symbiotic organism search (ESOS) algorithm with the aim of improving the exploitive capability of the SOS algorithm is presented. The enhancements are done in the commensalism and mutualism phases of the algorithm. In the current study, a multi-ecosystem mechanism is also employed. The presented ESOS algorithm is used along with two different constraint handling methods, namely mapping strategy (MS) and penalty function (PF). This leads to two variants of the ESOS, termed as ESOS-MS and ESOS-PF. The two presented ESOS variants are utilized to optimize the weight of four benchmark truss structures. The results are compared with the optimal designs reported in the literature. The results demonstrate that the proposed ESOS variants not only can find better solutions compared to the other algorithms, but also are faster in doing so. Moreover, of the two presented ESOS variants, the ESOS-MS variant performs much better than the ESOS-PF variant.
An Enhanced Symbiotic Organism Search Algorithm (ESOS) for the Sizing Design of Pin Connected Structures
https://orcid.org/http://orcid.org/0000-0002-5502-059X
The symbiotic organism search (SOS) algorithm is a strong metaheuristic search engine with a great explorative capability which enables it to search for global optima efficiently. Nevertheless, its exploitive ability is less profound leading to a relatively low rate of convergence. In the current paper, an enhanced symbiotic organism search (ESOS) algorithm with the aim of improving the exploitive capability of the SOS algorithm is presented. The enhancements are done in the commensalism and mutualism phases of the algorithm. In the current study, a multi-ecosystem mechanism is also employed. The presented ESOS algorithm is used along with two different constraint handling methods, namely mapping strategy (MS) and penalty function (PF). This leads to two variants of the ESOS, termed as ESOS-MS and ESOS-PF. The two presented ESOS variants are utilized to optimize the weight of four benchmark truss structures. The results are compared with the optimal designs reported in the literature. The results demonstrate that the proposed ESOS variants not only can find better solutions compared to the other algorithms, but also are faster in doing so. Moreover, of the two presented ESOS variants, the ESOS-MS variant performs much better than the ESOS-PF variant.
An Enhanced Symbiotic Organism Search Algorithm (ESOS) for the Sizing Design of Pin Connected Structures
Iran J Sci Technol Trans Civ Eng
Makiabadi, Mohammad H. (author) / Maheri, Mahmoud R. (author)
2021-09-01
26 pages
Article (Journal)
Electronic Resource
English
A novel many-objective symbiotic organism search algorithm for industrial engineering problems
| Springer Verlag | 2024
| British Library Online Contents | 1995