Models and Concepts of Life and Intelligence

Kennedy, James; Eberhart, Russell C.; Shi, Yuhui

doi:10.1016/b978-155860595-4/50001-2

Cited by 8 publications

(7 citation statements)

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“…These values are generally determined by understanding the nature of the problem by means of experimental studies. The choice of the termination criterion and the number of the iterations is problem‐dependent and some studies address these issues in detail (Engelbrecht, 2007; Kennedy et al., 2001; Pace et al., 2021). Here, we chose to use the Root‐Means‐Square‐Error (RMSE) with the following definition:

\text{RMSE}={\Vert {{\Delta }T}_{\text{obs}}-{{\Delta }T}_{\text{cal}}\Vert }_{2}/\sqrt{n}

where Δ T obs and Δ T cal denote the observed and calculated geomagnetic data, and n represents the number of observations.…”

Section: Methodsmentioning

confidence: 99%

Inversion of Geomagnetic Anomalies Caused by Ore Masses Using Hunger Games Search Algorithm

Ai,

Ekinci,

Balkaya

et al. 2023

Earth and Space Science

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Geomagnetic anomaly interpretation through inversion procedures often yields useful results in determining the key details of ore masses. However, the problem is complicated due to the known ambiguous phenomena of the inversion process. Thus, details such as location, depth and shape can only be estimated using an efficient algorithm. To this end, we presented here a novel global optimization algorithm called Hunger Games Search (HGS) for the inversion of geomagnetic anomalies caused by ore masses. HGS is a well‐organized metaheuristic inspired by the hunger‐driven instincts and social behavioral decisions of animals. This is the first work in the literature to introduce this optimizer for the inversion of geophysical anomalies. We revealed the model parameter dependencies and the optimum control parameter values of the algorithm by performing some modal analyses and parameter tuning studies, respectively. The capability of HGS was demonstrated on synthetically produced geomagnetic responses and on three real anomalies obtained from some exploration fields in India and the USA. Uncertainty appraisal studies showed the solidity of the outputs of the algorithm. Moreover, some comparative studies with the fine‐tuned standard Particle Swarm Optimization, a commonly used tool for the inversion of geophysical anomalies, indicated that HGS algorithm provides better results in terms of convergence characteristics and solution stabilities in the presented inverse problem. We therefore recommend the use of this metaheuristic in model parameter estimation studies when dealing with this kind of geophysical potential field problems.

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\text{RMSE}={\Vert {{\Delta }T}_{\text{obs}}-{{\Delta }T}_{\text{cal}}\Vert }_{2}/\sqrt{n}

where Δ T obs and Δ T cal denote the observed and calculated geomagnetic data, and n represents the number of observations.…”

Section: Methodsmentioning

confidence: 99%

Inversion of Geomagnetic Anomalies Caused by Ore Masses Using Hunger Games Search Algorithm

Ai,

Ekinci,

Balkaya

et al. 2023

Earth and Space Science

View full text Add to dashboard Cite

show abstract

“…The way platoons are formed is based on the fields of swarm robotics and flocking, which are inspired by nature, more precisely, by the way fish, birds [14], insects, and mammals interact in a group [15,16]. An individual possesses poor abilities, but within a group, they contribute to the formation of complex group behaviors and, thus, provide flexibility and robustness, such as route planning and task allocation.…”

Section: Traffic Optimizationmentioning

confidence: 99%

“…To determine the position relative to Vehicle 1, the other vehicle needs to compute the distance traveled by the first vehicle in the time interval from timestamp T1 to the current timestamp T2, taking into account its speed. To compute the distance as accurately as possible, the speed reported at each timestamp is considered, and the calculation formula is presented in Equation (16). Since Frame V1 is the x-th frame at timestamp T1, and considering that there are 30 frames per second, the time remaining until timestamp T1 + 1 second can be determined.…”

Section: Compute Distancementioning

confidence: 99%

Architectural Framework to Enhance Image-Based Vehicle Positioning for Advanced Functionalities

Beti,

Herghelegiu,

Caruntu

2024

Information

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The growing number of vehicles on the roads has resulted in several challenges, including increased accident rates, fuel consumption, pollution, travel time, and driving stress. However, recent advancements in intelligent vehicle technologies, such as sensors and communication networks, have the potential to revolutionize road traffic and address these challenges. In particular, the concept of platooning for autonomous vehicles, where they travel in groups at high speeds with minimal distances between them, has been proposed to enhance the efficiency of road traffic. To achieve this, it is essential to determine the precise position of vehicles relative to each other. Global positioning system (GPS) devices have an intended positioning error that might increase due to various conditions, e.g., the number of available satellites, nearby buildings, trees, driving into tunnels, etc., making it difficult to compute the exact relative position between two vehicles. To address this challenge, this paper proposes a new architectural framework to improve positioning accuracy using images captured by onboard cameras. It presents a novel algorithm and performance results for vehicle positioning based on GPS and video data. This approach is decentralized, meaning that each vehicle has its own camera and computing unit and communicates with nearby vehicles.

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“…PSO is a kind of self-adaptive random algorithm based on a group hunting strategy [57,58]. The particles are initially randomized in the given ranges followed by an iterative search for optima.…”

Section: Psomentioning

confidence: 99%

Parameter Optimization for Uncertainty Reduction and Simulation Improvement of Hydrological Modeling

Hui

Zhao

et al. 2020

Remote Sensing

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Hydrological modeling has experienced rapid development and played a significant role in water resource management in recent decades. However, modeling uncertainties, which are propagated throughout model runs, may affect the credibility of simulation results and mislead management decisions. Therefore, analyzing and reducing uncertainty is of significant importance in providing greater confidence in hydrological simulations. To reduce and quantify parameter uncertainty, in this study, we attempted to introduce additional remotely sensed data (such as evapotranspiration (ET)) into a common parameter estimation procedure that uses observed streamflow only. We undertook a case study of an application of the Soil Water Assessment Tool in the Guijiang River Basin (GRB) in China. We also compared the effects of different combinations of parameter estimation algorithms (e.g., Sequential Uncertainty Fitting version 2, particle swarm optimization) on reduction in parameter uncertainty and improvement in modeling precision improvement. The results indicated that combining Sequential Uncertainty Fitting version 2 (SUFI-2) and particle swarm optimization (PSO) can substantially reduce the modeling uncertainty (reduction in the R-factor from 0.9 to 0.1) in terms of the convergence of parameter ranges and the aggregation of parameters, in addition to iterative optimization. Furthermore, the combined approaches ensured the rationality of the parameters’ physical meanings and reduced the complexity of the model calibration procedure. We also found the simulation accuracy of ET improved substantially after adding remotely sensed ET data. The parameter ranges and optimal parameter sets obtained by multi-objective calibration (using streamflow plus ET) were more reasonable and the Nash–Sutcliffe coefficient (NSE) improved more rapidly using multiple objectives, indicating a more efficient parameter optimization procedure. Overall, the selected combined approach with multiple objectives can help reduce modeling uncertainty and attain a reliable hydrological simulation. The presented procedure can be applied to any hydrological model.

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Models and Concepts of Life and Intelligence

Cited by 8 publications

References 0 publications

Inversion of Geomagnetic Anomalies Caused by Ore Masses Using Hunger Games Search Algorithm

Inversion of Geomagnetic Anomalies Caused by Ore Masses Using Hunger Games Search Algorithm

Architectural Framework to Enhance Image-Based Vehicle Positioning for Advanced Functionalities

Parameter Optimization for Uncertainty Reduction and Simulation Improvement of Hydrological Modeling

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