An evolutionary symbiotic organisms search for multiconstraint truss optimization under free vibration and transient behavior

2021 ◽  
Vol 160 ◽  
pp. 103045
Author(s):  
Sy Nguyen-Van ◽  
Khoa T. Nguyen ◽  
Khanh D. Dang ◽  
Nga T.T. Nguyen ◽  
Seunghye Lee ◽  
...  
Keyword(s):  
Free Vibration ◽  
Transient Behavior ◽  
Truss Optimization ◽  
Symbiotic Organisms Search ◽  
Symbiotic Organisms

scholarly journals Truss optimization with natural frequency bounds using improved symbiotic organisms search

2018 ◽  
Vol 143 ◽  
pp. 162-178 ◽  
Author(s):  
Ghanshyam G. Tejani ◽  
Vimal J. Savsani ◽  
Vivek K. Patel ◽  
Seyedali Mirjalili
Keyword(s):  
Natural Frequency ◽  
Truss Optimization ◽  
Symbiotic Organisms Search ◽  
Symbiotic Organisms

Multiobjective adaptive symbiotic organisms search for truss optimization problems

2018 ◽  
Vol 161 ◽  
pp. 398-414 ◽  
Author(s):  
Ghanshyam G. Tejani ◽  
Nantiwat Pholdee ◽  
Sujin Bureerat ◽  
Doddy Prayogo
Keyword(s):  
Optimization Problems ◽  
Truss Optimization ◽  
Symbiotic Organisms Search ◽  
Symbiotic Organisms

An enhanced symbiotic organisms search algorithm for design optimization of trusses with frequency constraints

2021 ◽  
pp. 136943322110262
Author(s):  
Mohammad H Makiabadi ◽  
Mahmoud R Maheri
Keyword(s):  
Standard Deviation ◽  
Optimization Problems ◽  
Search Algorithm ◽  
Metaheuristic Algorithms ◽  
Minimum Mass ◽  
Multiple Frequency ◽  
Frequency Constraints ◽  
Symbiotic Organisms Search ◽  
Symbiotic Organisms ◽  
Dynamic Frequency

An enhanced symbiotic organisms search (ESOS) algorithm is developed and presented. Modifications to the basic symbiotic organisms search algorithm are carried out in all three phases of the algorithm with the aim of balancing the exploitation and exploration capabilities of the algorithm. To verify validity and capability of the ESOS algorithm in solving general optimization problems, the CEC2014 set of 22 benchmark functions is first optimized and the results are compared with other metaheuristic algorithms. The ESOS algorithm is then used to optimize the sizing and shape of five benchmark trusses with multiple frequency constraints. The best (minimum) mass, mean mass, standard deviation of the mass, total number of function evaluations, and the values of frequency constraints are then compared with those of a number of other metaheuristic solutions available in the literature. It is shown that the proposed ESOS algorithm is generally more efficient in optimizing the shape and sizing of trusses with dynamic frequency constraints compared to other reported metaheuristic algorithms, including the basic symbiotic organisms search and its other recently proposed improved variants such as the improved symbiotic organisms search algorithm (ISOS) and modified symbiotic organisms search algorithm (MSOS).

Biomedical Document Clustering Based on Accelerated Symbiotic Organisms Search Algorithm

2021 ◽  
Vol 12 (4) ◽  
pp. 169-185
Author(s):  
Saida Ishak Boushaki ◽  
Omar Bendjeghaba ◽  
Nadjet Kamel
Keyword(s):  
Clustering Algorithm ◽  
Search Algorithm ◽  
Clustering Algorithms ◽  
Document Clustering ◽  
Latent Semantic Indexing ◽  
Research Area ◽  
Semantic Indexing ◽  
Local Optima ◽  
Symbiotic Organisms Search ◽  
Symbiotic Organisms

Clustering is an important unsupervised analysis technique for big data mining. It finds its application in several domains including biomedical documents of the MEDLINE database. Document clustering algorithms based on metaheuristics is an active research area. However, these algorithms suffer from the problems of getting trapped in local optima, need many parameters to adjust, and the documents should be indexed by a high dimensionality matrix using the traditional vector space model. In order to overcome these limitations, in this paper a new documents clustering algorithm (ASOS-LSI) with no parameters is proposed. It is based on the recent symbiotic organisms search metaheuristic (SOS) and enhanced by an acceleration technique. Furthermore, the documents are represented by semantic indexing based on the famous latent semantic indexing (LSI). Conducted experiments on well-known biomedical documents datasets show the significant superiority of ASOS-LSI over five famous algorithms in terms of compactness, f-measure, purity, misclassified documents, entropy, and runtime.

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