Symbiotic organisms search and two solution representations for solving the capacitated vehicle routing problem

Yu, Vincent F.; Redi, Anak Agung Ngurah Perwira; Yang, Chao-Lung; Ruskartina, Eki; Santosa, Budi

doi:10.1016/j.asoc.2016.10.006

Cited by 91 publications

(25 citation statements)

References 46 publications

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“…Initially developed by Cheng and Prayogo [10], the SOS algorithm took its inspiration from the symbiotic interactions among a group of organisms. Its initial application was to solve continuous optimization problems [10] and it has been used to solve various problems in multiple disciplines [19][20][21][22][23][24][25][26][27]. SOS utilized nature-inspired operators -the mutualism phase, commensalism phase, and parasitism phase -to guide the organisms (solutions) to the global optima region (best solution).…”

Section: Optimization Algorithm: Sosmentioning

confidence: 99%

Optimizing the prediction accuracy of load-settlement behavior of single pile using a self-learning data mining approach

Prayogo

Susanto

2019

MATEC Web Conf.

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Pile foundations usually are used when the upper soil layers are soft clay and, hence, unable to support the structures’ loads. Piles are needed to carry these loads deep into the hard soil layer. Therefore, the safety and stability of pile-supported structures depends on the behavior of the piles. Additionally, an accurate prediction of the piles’ behavior is very important to ensure satisfactory performance of the structures. Although many methods in the literature estimate the settlement of the piles both theoretically and experimentally, methods for comprehensively predicting the load-settlement of piles are very limited. This study develops a new data mining approach called self-learning support vector machine (SL-SVM) to predict the load-settlement behavior of single piles. SL-SVM performance is investigated using 446 training data points and 53 test data points of cone penetration test (CPT) data obtained from the previous literature. The actual prediction accuracy is then compared to other prediction methods using three statistical measurements, including mean absolute error (MAE), coefficient of correlation (R), and root mean square error (RMSE). The obtained results show that SL-SVM achieves better accuracy than does LS-SVM and BPNN. This confirms the capability of the proposed data mining method to model the accurate load-settlement behavior of single piles through CPT data. The paper proposes beneficial insights for geotechnical engineers involved in estimating pile behavior.

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Section: Optimization Algorithm: Sosmentioning

confidence: 99%

Optimizing the prediction accuracy of load-settlement behavior of single pile using a self-learning data mining approach

Prayogo

Susanto

2019

MATEC Web Conf.

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“…Similar to other typical metaheuristic algorithms, the SOS algorithm leads to the optimization process of the candidate solutions through special search operators. The SOS algorithm has been used for solving optimization problems in different areas of research from the time it was developed in 2014 [11,[14][15][16][17][18][19][20].…”

Section: Symbiotic Organisms Searchmentioning

confidence: 99%

Metaheuristic-Based Machine Learning System for Prediction of Compressive Strength based on Concrete Mixture Properties and Early-Age Strength Test Results

Prayogo¹

2018

ced

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Estimating the accurate concrete strength has become a critical issue in civil engineering. The 28-day concrete cylinder test results depict the concrete's characteristic strength which was prepared and cast as part of the concrete work on the project. Waiting 28 days is important to guarantee the quality control of the procedure, even though it is a slow process. This research develops an advanced machine learning method to forecast the concrete compressive strength using the concrete mix proportion and early-age strength test results. Thirty-eight historical cases in total were used to create the intelligence prediction method. The results obtained indicate the effectiveness of the advanced hybrid machine learning strategy in forecasting the strength of the concrete with a comparatively high degree of accuracy calculated using 4 error indicators. As a result, the suggested study can provide a great advantage for construction project managers in decision-making procedures that depend on early strength results of the tests.

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“…Inadequate parameter tuning is likely to lead to a finding of the obtained solutions in local optima regions. The SOS has been applied to optimization problem solving in many research areas since its development in 2014 [11][12][13][14][15][16][17].…”

Section: Hyper-parameter Tuning: Symbiotic Organisms Search Optimizatmentioning

confidence: 99%

Prediction of High-Performance Concrete Strength Using a Hybrid Artificial Intelligence Approach

2018

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This study introduces an improved artificial intelligence (AI) approach called intelligence optimized support vector regression (IO-SVR) for estimating the compressive strength of high-performance concrete (HPC). The nonlinear functional mapping between the HPC materials and compressive strength is conducted using the AI approach. A dataset with 1,030 HPC experimental tests is used to train and validate the prediction model. Depending on the results of the experiments, the forecast outcomes of the IO-SVR model are of a much higher quality compared to the outcomes of other AI approaches. Additionally, because of the high-quality learning capabilities, the IO-SVR is highly recommended for calculating HPC strength.

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Symbiotic organisms search and two solution representations for solving the capacitated vehicle routing problem

Cited by 91 publications

References 46 publications

Optimizing the prediction accuracy of load-settlement behavior of single pile using a self-learning data mining approach

Optimizing the prediction accuracy of load-settlement behavior of single pile using a self-learning data mining approach

Metaheuristic-Based Machine Learning System for Prediction of Compressive Strength based on Concrete Mixture Properties and Early-Age Strength Test Results

Prediction of High-Performance Concrete Strength Using a Hybrid Artificial Intelligence Approach

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