Pareto-Based Multiobjective Particle Swarm Optimization: Examples in Geophysical Modeling

Buyuk, Ersin

doi:10.5772/intechopen.97067

Cited by 5 publications

(3 citation statements)

References 37 publications

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“…In addition, the deviation angle criteria are used to evaluate the compatibility of the objective function terms. For more details on the methods used to improve the quality of the solutions during the MOPSO process, evaluate the compatibility of the objectives, and the performance of the algorithm in the final iteration, see previous studies (Büyük, 2021; Coello Coello et al., 2004; Dal Moro, 2010; Pace et al., 2019a; Pace et al., 2021; Reyes‐Sierra & Coello Coello, 2006; Schnaidt et al., 2018).…”

Section: Multi‐objective Particle Swarm Optimization Based On the Par...mentioning

confidence: 99%

A new method of smoothness‐constrained magnetotelluric modelling with the utility of Pareto‐optimal multi‐objective particle swarm optimization

Büyük

2024

Geophysical Prospecting

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Particle swarm optimization, one of the modern global optimization methods, is attracting widespread interest because it overcomes the difficulties of conventional inversion techniques, such as trapping at a local minimum and/or initial model dependence. The main characteristic of particle swarm optimization is the large search space of parameters, which in a sense allows the exploration of the entire objective function space if the input parameters are properly chosen. However, in the case of a high‐dimensional model space, the numerical instability of the solution may increase and lead to unrealistic models and misinterpretations due to the sampling problem of particle swarm optimization. Therefore, smoothness‐constrained regularization techniques used for the objective function or model reduction techniques are required to stabilize the solution. However, weighting and combining objective function terms is partly a subjective process, as the regularization parameter is generally chosen based on some kind of criteria of how the smoothing constraints affect the data misfits. This means that it cannot be completely predefined but needs to be adjusted during the inversion process, which begins with the response of an initial model. In this paper, a new modelling approach is proposed to obtain a smoothness‐constrained model from magnetotelluric data utilizing multi‐objective particle swarm optimization based on the Pareto optimality approach without using a regularization parameter and combining several objective function terms. The presented approach was verified on synthetic models and an application with field data set from the Çanakkale–Tuzla geothermal field in Turkey. Findings from these analyses confirm the usefulness of the method as a new approach for all constrained inversions of geophysical data without the need to combine the objective function terms weighted by a regularization parameter.

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Section: Multi‐objective Particle Swarm Optimization Based On the Par...mentioning

confidence: 99%

A new method of smoothness‐constrained magnetotelluric modelling with the utility of Pareto‐optimal multi‐objective particle swarm optimization

Büyük

2024

Geophysical Prospecting

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“…Using optimization has been extended to be used for seismological modeling. In [153], the authors proposed a multi-objective…”

Section: A Optimization and Seismic Signalsmentioning

confidence: 99%

“…Newton's theorem [58]- [66] Secant theorem [67]- [71] Lagrange multiplier [72]- [78] Numerical Linear programming [79]- [85] Integer programming [57], [86]- [92] Quadratic programming [93]- [101] Non-linear programming [102]- [107] Stochastic programming [108]- [112] Dynamic programming [113]- [118] Combinatorial [77], [119]- [123] Advanced Genetic algorithm (GA) [124]- [130] Particle swarm [131]- [135] Karush-Kuhn-Tucker (KKT) [136]- [138] Simulated annealing [139]- [142] Ant colony [143]- [148] [3], [57], [154]- [162].…”

Section: Classicalmentioning

confidence: 99%

Employing Remote Sensing, Data Communication Networks, AI, and Optimization Methodologies in Seismology

Abdalzaher

El-Sayed

Fouda

2022

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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Seismology is among the intrinsic sciences that strictly affect human lives. Many research efforts are presented in the literature aiming at achieving risk mitigation and disaster management. More particularly, modern technologies have been employed in such a pivot. However, the day-to-day challenges and complexities of such natural science that face the stack holders still need more reliable and intelligent solutions. The solution can depend on a partial or integrated system of modern technologies. In this paper, we extensively survey the co-related modern technologies aiming to gather the major efforts exerted in this regard. It also outlines the desirability of seismology to modern technologies. Then, we present a detailed analysis of remote sensing and data communication networks (DCNs), which are considered the backend of seismic networks. Furthermore, for seismology, we depict both classical and non-classical approaches based on DCN principles, such as optical fiber-based acoustic sensors, social media, and the internet of things (IoT). Following that, a comprehensive description of the various optimization techniques utilized for seismic wave analysis and for prolonging network lifetime is offered. A description of the important functions that artificial intelligence (AI) can play in different fields of seismology is also included. Finally, we present some recommendations for stack holders to prevent natural calamities and preserve human lives.

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A Guide to Meta-Heuristic Algorithms for Multi-objective Optimization: Concepts and Approaches

Banerjee,

Pradhan,

Misra

et al. 2024

Springer Tracts in Nature-Inspired Computing

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Pareto-Based Multiobjective Particle Swarm Optimization: Examples in Geophysical Modeling

Cited by 5 publications

References 37 publications

A new method of smoothness‐constrained magnetotelluric modelling with the utility of Pareto‐optimal multi‐objective particle swarm optimization

A new method of smoothness‐constrained magnetotelluric modelling with the utility of Pareto‐optimal multi‐objective particle swarm optimization

Employing Remote Sensing, Data Communication Networks, AI, and Optimization Methodologies in Seismology

A Guide to Meta-Heuristic Algorithms for Multi-objective Optimization: Concepts and Approaches

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