A novel Multiple Objective Symbiotic Organisms Search (MOSOS) for time–cost–labor utilization tradeoff problem

Tran, Duc-Hoc; Cheng, Min‐Yuan; Prayogo, Doddy

doi:10.1016/j.knosys.2015.11.016

Cited by 139 publications

(54 citation statements)

References 37 publications

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“…With improper tuning of the parameters, there is a possibility that the obtained solutions are found in local optima regions. Since the first development in 2014, SOS has been successfully utilized in solving many optimization problems in various research areas [25][26][27][28][29][30][31].…”

Section: Metaheuristic Optimization Algorithm: Symbiotic Organisms Sementioning

confidence: 99%

Optimizing the Prediction Accuracy of Friction Capacity of Driven Piles in Cohesive Soil Using a Novel Self‐Tuning Least Squares Support Vector Machine

Prayogo

Susanto

2018

Advances in Civil Engineering

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is research presents a novel hybrid prediction technique, namely, self-tuning least squares support vector machine (ST-LSSVM), to accurately model the friction capacity of driven piles in cohesive soil.e hybrid approach uses LS-SVM as a supervised-learning-based predictor to build an accurate input-output relationship of the dataset and SOS method to optimize the σ and c parameters of the LS-SVM. Evaluation and investigation of the ST-LSSVM were conducted on 45 training data and 20 testing data of driven pile load tests that were compiled from previous studies. e prediction accuracy of the ST-LSSVM was then compared to other machine learning methods, namely, LS-SVM and BPNN, and was benchmarked with the previous results by neural network (NN) from Goh using coefficient of correlation (R), mean absolute error (MAE), and root mean square error (RMSE). e comparison showed that the ST-LSSVM performed better than LS-SVM, BPNN, and NN in terms of R, RMSE, and MAE. is comprehensive evaluation confirmed the capability of hybrid approach SOS and LS-SVM to modeling the accurate friction capacity of driven piles in clay. It makes for a reliable and robust assistance tool in helping all geotechnical engineers estimate friction pile capacity.

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Section: Metaheuristic Optimization Algorithm: Symbiotic Organisms Sementioning

confidence: 99%

Optimizing the Prediction Accuracy of Friction Capacity of Driven Piles in Cohesive Soil Using a Novel Self‐Tuning Least Squares Support Vector Machine

Prayogo

Susanto

2018

Advances in Civil Engineering

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“…Algorithm-specific parameters for other competing algorithms might increase computational time and produce local optimal solutions. Hence, SOS has been successfully applied to various types of problem such as the construction management [54], work shift [55], and optimal reservoir operation [56]. Figure 3 shows the flowchart of an SOS algorithm.…”

Section: Symbiotic Organism Search (Sos)mentioning

confidence: 99%

A Hybrid Heuristic Optimization Approach for Leak Detection in Pipe Networks Using Ordinal Optimization Approach and the Symbiotic Organism Search

Lin

2017

Water

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The OOA is applied to sift through the candidate pipes and the initial organisms with leak information. The SOS is employed to determine the leaks by minimizing the sum of differences between simulated and computed head at the observation points. Two synthetic leaking scenarios, a simple pipe network and a water distribution network (WDN), are chosen to test the performance of leak detection ordinal symbiotic organism search (LDOSOS). Leak information can be accurately identified by the proposed approach for both of the scenarios. The presented technique makes a remarkable contribution to the success of leak detection in the pipe networks.

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“…For more details, the reader can refer to [8]. Since the first publication in 2014, the use of SOS in solving different problems has been significantly increased in various research topics [19][20][21][22][23][24][25][26][27]. It shows the potential of SOS algorithm as an attractive tool to search for the optimality.…”

Section: Symbiotic Organisms Searchmentioning

confidence: 99%

Prediction of Concrete Compressive Strength from Early Age Test Result Using an Advanced Metaheuristic-Based Machine Learning Technique

Prayogo¹,

Cheng²,

Widjaja³

et al. 2017

Proceedings of the International Symposium on Automation and Robotics in Construction (IAARC)

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-Determining accurate concrete strength is a major civil engineering problem. Test results of 28-day concrete cylinder represent the characteristic strength of the concrete that has been prepared and cast to form the concrete work. It is important to wait 28 days to ensure the quality control of the process, although it is very time consuming. Machine learning techniques are progressively used to simulate the characteristic of concrete materials and have developed into an important research area. This study proposed a comprehensive study using an advanced machine learning technique to predict the compressive strength of concrete from early age test results. In this case, early age test data are being used to get reliable values of the two constants which are required for the prediction. A total of 28 historical cases were used to establish the intelligence prediction model. Obtained results show the performance of the advanced hybrid machine learning technique in predicting the concrete strength with a relatively high accuracy measured by four error indicators. Therefore, the proposed study can offer a high benefit for construction project managers in decision-making processes based on early strength test results.

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A novel Multiple Objective Symbiotic Organisms Search (MOSOS) for time–cost–labor utilization tradeoff problem

Cited by 139 publications

References 37 publications

Optimizing the Prediction Accuracy of Friction Capacity of Driven Piles in Cohesive Soil Using a Novel Self‐Tuning Least Squares Support Vector Machine

Optimizing the Prediction Accuracy of Friction Capacity of Driven Piles in Cohesive Soil Using a Novel Self‐Tuning Least Squares Support Vector Machine

A Hybrid Heuristic Optimization Approach for Leak Detection in Pipe Networks Using Ordinal Optimization Approach and the Symbiotic Organism Search

Prediction of Concrete Compressive Strength from Early Age Test Result Using an Advanced Metaheuristic-Based Machine Learning Technique

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