Applications of Particle Swarm Optimization in Geotechnical Engineering: A Comprehensive Review

Kalatehjari, Roohollah; Hajihassani, Mohsen; Armaghani, Danial Jahed

doi:10.1007/s10706-017-0356-z

Cited by 151 publications

(48 citation statements)

References 87 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…The PSO algorithm which is a meta heuristic algorithm based on the social behaviour of birds striving to achieve a goal was developed by Kennedy and Eberhart [11,35]. PSO algorithm has been used in different fields such as industry engineering [36], civil engineering [37], energy systems engineering [38], electrical engineering [39] and geology engineering [40] because of its successful performance. Qu and Lou [41] used the PSO algorithm for the optimal allocation of regional water resources.…”

Section: Particle Swarm Optimization Algorithm (Pso)mentioning

confidence: 99%

Solution of Multiple Travelling Salesman Problem using Particle Swarm Optimization based Algorithms

Gulcu¹,

Kahramanlı²

2019

ijisae

View full text Add to dashboard Cite

Nowadays, the systems that are inspired by biological structures have gained importance and attracted the attention of researchers. The Multiple Travelling Salesman Problem (MTSP) is an extended version of the TSP. The aim in the MTSP is to find the tours for m salesmen, who all start and end at the depot, such that each intermediate node is visited exactly once and the total cost of visiting nodes is minimized. The Particle Swarm Optimization (PSO) algorithm which is a meta-heuristic algorithm based on the social behaviour of birds. In this article, 2 algorithms based on PSO, called APSO and HAPSO, were proposed to solve the MTSP. The APSO algorithm is based on the PSO and 2-opt algorithms, the path-relink and swap operators. While the HAPSO algorithm is based on the GRASP, PSO and 2-opt algorithms, the path-relink and swap operators. In the experiments, 5 TSP instances are used and the algorithms are compared with the GA and ACO algorithms. According to the results, the HAPSO algorithm has the better performance than the other algorithms on the most instances. Moreover the HAPSO algorithm produces more stable results than the APSO algorithm and the performance of the HAPSO algorithm is better in all the MTSP instances. Therefore, the HAPSO algorithm is more robust than the APSO algorithm.

show abstract

Section: Particle Swarm Optimization Algorithm (Pso)mentioning

confidence: 99%

Solution of Multiple Travelling Salesman Problem using Particle Swarm Optimization based Algorithms

Gulcu¹,

Kahramanlı²

2019

ijisae

View full text Add to dashboard Cite

show abstract

“…The 1 and 2 components of the equation are randomly generated numbers within the range of 0 and 1. The readers may refer to [17,34,35,43,46,47] for more information and further reading on PSO and ANFIS.…”

Section: Optimisation Of Anfis Model With Psomentioning

confidence: 99%

“…Fuzzy inference systems (FIS) and artificial neural network (ANN) are very versatile in the estimation of the system behaviour [15,16]. Optimization of an adaptive neuro-fuzzy inference systems (ANFIS) with particle swarm optimisation (PSO) has been tailored to produce several excellent results for different engineering applications [14,17,18]. ANFIS hybridized with PSO optimizes the adaptive ANFIS layers, thus achieving optimal ANFIS parameters.…”

mentioning

confidence: 99%

“…PSO has been credited as a powerful and intelligent population-driven computational algorithm which is able to solve a problem using the population of candidate solutions [19]. PSO acceptance is due to its many benefits which include fast convergence, high performance on nonlinear functions optimisation, simplicity of operation, and ease of realization [17,20]. PSO has been successfully applied in various field which include engineering, management, medicine, pharmaceuticals, safety and so on [19,21,22].…”

mentioning

confidence: 99%

See 1 more Smart Citation

Estimation of Municipal Solid Waste (MSW) combustion enthalpy for energy recovery

Olatunji¹,

Akinlabi²,

Madushele³

et al. 2019

EAI Endorsed Transactions on Energy Web

View full text Add to dashboard Cite

The global challenges of climate change have been compounded by an unprecedented level of environmental pollution consequent upon the municipal solid waste, MSW generation. Recent advances by researchers and policymakers are focused on sustainable and renewable energy sources which are technologically feasible, environmentally friendly, and economically viable. Waste-to-fuel initiative is therefore highly beneficial to our environment while also improves the socioeconomic well-being the nations. This current study introduces an adaptive neuro-fuzzy inference systems (ANFIS) model optimised with Particle Swarm Optimisation (PSO) algorithm aimed at predicting the enthalpy of combustion of MSW fuel based on the moisture content (H 2 O), Carbon, Hydrogen, Oxygen, Nitrogen, Sulphur, and Ash contents. This model was trained with 86 MSW biomass data and further tested with a new 37 data points. The developed model was observed to performed better in term of the accuracy when compared with other existing models in the literature. The model was evaluated based on some known error estimation. The values of Root Mean Squared Error (RMSE), Mean Absolute Deviation (MAD), Mean Absolute Percentage Error (MAPE), Log Accuracy ratio (LAR), Coefficient of Correlation (CC) were 3.6277, 22.6202, 0.0337, 0.8673 respectively at computation time (CT) of 36.96 secs. Regression analysis was also carried out to determine the level of correlation between the experimental and predicted high heating values (HHV).

show abstract

“…During the initialization phase, a certain number of individuals (i.e., the particles each of which contains feasible solution) are placed in a random pattern within the search domain. The optimization of the objective function is determined with the help of pre-defined coefficients: C 1 and C 2 signify the personal best position (p best ) of each individual particle and global best position (g best ) among the populated particles, respectively [30].…”

mentioning

confidence: 99%

Developing a New Computational Intelligence Approach for Approximating the Blast-Induced Ground Vibration

Kumar

Samui

et al. 2020

Applied Sciences

View full text Add to dashboard Cite

Ground vibration induced by blasting operations is an important undesirable effect in surface mines and has significant environmental impacts on surrounding areas. Therefore, the precise prediction of blast-induced ground vibration is a challenging task for engineers and for managers. This study explores and evaluates the use of two stochastic metaheuristic algorithms, namely biogeography-based optimization (BBO) and particle swarm optimization (PSO), as well as one deterministic optimization algorithm, namely the DIRECT method, to improve the performance of an artificial neural network (ANN) for predicting the ground vibration. It is worth mentioning this is the first time that BBO-ANN and DIRECT-ANN models have been applied to predict ground vibration. To demonstrate model reliability and effectiveness, a minimax probability machine regression (MPMR), extreme learning machine (ELM), and three well-known empirical methods were also tested. To collect the required datasets, two quarry mines in the Shur river dam region, located in the southwest of Iran, were monitored, and the values of input and output parameters were measured. Five statistical indicators, namely the percentage root mean square error (%RMSE), coefficient of determination (R2), Ratio of RMSE to the standard deviation of the observations (RSR), mean absolute error (MAE), and degree of agreement (d) were taken into account for the model assessment. According to the results, BBO-ANN provided a better generalization capability than the other predictive models. As a conclusion, BBO, as a robust evolutionary algorithm, can be successfully linked to the ANN for better performance.

show abstract

Applications of Particle Swarm Optimization in Geotechnical Engineering: A Comprehensive Review

Cited by 151 publications

References 87 publications

Solution of Multiple Travelling Salesman Problem using Particle Swarm Optimization based Algorithms

Solution of Multiple Travelling Salesman Problem using Particle Swarm Optimization based Algorithms

Estimation of Municipal Solid Waste (MSW) combustion enthalpy for energy recovery

Developing a New Computational Intelligence Approach for Approximating the Blast-Induced Ground Vibration

Contact Info

Product

Resources

About