Evaluation of Flood Routing Models and Their Relationship to The Hydraulic Properties of The Diyala River Bed

Abstract In this study, four types of flood routing approaches were studied which give significantly varied results represented by the differences between computed and observed flows and also differ considerably on the friction coefficient and bed slope of the channels. First two approaches use a hydraulic solution to solve the equations of unsteady flow, while the third approach uses the hydrological solution, and the fourth algorithm solves Muskingum approach with seven parameters. All these approaches were run with the same input parameters, the results were compared and tested with four Error Measurement Indices, Sum of Squared Deviations, Error of Peak Discharge, Variance Index, and agreement index. Diyala River was selected for this application. Dynamic wave method gave accurate results, followed by the characteristic method, and then the linear Muskingum-Cunge method, but Symbiotic Organisms Search Algorithm not gave any senses due to change in roughness or bed slope and gave very identical values with recorded outflow in all conditions, which means that the hydraulic solution is better compared to the hydrological solution. The results also showed that the difference between the calculated and observed flows diminished with a decrease in the coefficient of friction and an increase in the bed slope channel.

Download Full-text

An efficient binary chaotic symbiotic organisms search algorithm approaches for feature selection problems

The Journal of Supercomputing ◽

10.1007/s11227-021-03626-6 ◽

2021 ◽

Author(s):

Hekmat Mohmmadzadeh ◽

Farhad Soleimanian Gharehchopogh

Keyword(s):

Feature Selection ◽

Search Algorithm ◽

Symbiotic Organisms Search ◽

Selection Problems ◽

Symbiotic Organisms

Download Full-text

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

Advances in Structural Engineering ◽

10.1177/13694332211026219 ◽

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).

Download Full-text

Symbiotic organisms search algorithm using random walk and adaptive Cauchy mutation on the feature selection of sleep staging

Expert Systems with Applications ◽

10.1016/j.eswa.2021.114887 ◽

2021 ◽

pp. 114887

Author(s):

Fahui Miao ◽

Li Yao ◽

Xiaojie Zhao

Keyword(s):

Feature Selection ◽

Random Walk ◽

Search Algorithm ◽

Sleep Staging ◽

Symbiotic Organisms Search ◽

Cauchy Mutation ◽

Symbiotic Organisms ◽

Selection Of

Download Full-text

Biomedical Document Clustering Based on Accelerated Symbiotic Organisms Search Algorithm

International Journal of Swarm Intelligence Research ◽

10.4018/ijsir.2021100109 ◽

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.

Download Full-text