A Comparison of Simulated Annealing, Genetic Algorithm and Particle Swarm Optimization in Optimal First-Order Design of Indoor TLS Networks

Jia, Fengman; Lichti, Derek D.

doi:10.5194/isprs-annals-iv-2-w4-75-2017

Cited by 45 publications

(46 citation statements)

References 32 publications

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“…As, the problem in that study is solved with a deterministic continuous optimization method, heuristic algorithms such as PSO, genetic algorithm, or simulated annealing algorithm can also be used in future studies. It should also be noted that there are other studies comparing the performance of heuristic methods for optimization problems . In addition, regarding the study of Abbasi et al, the ENMI is stated as the best choice for the cluster evaluation which can be adapted to the proposed model in this study for a future work.…”

Section: Conclusion and Discussionmentioning

confidence: 93%

“…It should also be noted that there are other studies comparing the performance of heuristic methods for optimization problems. [68][69][70] In addition, regarding the study of Abbasi et al, 52 the ENMI is stated as the best choice for the cluster evaluation which can be adapted to the proposed model in this study for a future work.…”

Section: Conclusion and Discussionmentioning

confidence: 96%

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Ensemble cluster pruning via convex‐concave programming

Özöğür‐Akyüz

Otar

Ataş

2020

Computational Intelligence

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Summary Ensemble learning is the process of aggregating the decisions of different learners/models. Fundamentally, the performance of the ensemble relies on the degree of accuracy in individual learner predictions and the degree of diversity among the learners. The trade‐off between accuracy and diversity within the ensemble needs to be optimized to provide the best grouping of learners as it relates to their performance. In this optimization theory article, we propose a novel ensemble selection algorithm which, focusing specifically on clustering problems, selects the optimal subset of the ensemble that has both accurate and diverse models. Those ensemble selection algorithms work for a given number of the best learners within the subset prior to their selection. The cardinality of a subset of the ensemble changes the prediction accuracy. The proposed algorithm in this study determines both the number of best learners and also the best ones. We compared our prediction results to recent ensemble clustering selection algorithms by the number of cardinalities and best predictions, finding better and approximated results to the optimum solutions.

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Section: Conclusion and Discussionmentioning

confidence: 93%

Section: Conclusion and Discussionmentioning

confidence: 96%

Ensemble cluster pruning via convex‐concave programming

Özöğür‐Akyüz

Otar

Ataş

2020

Computational Intelligence

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“…Several publications have focused on the TLS-related FOD aiming at viewpoint planning (i.e., the optimal instrument positioning to achieve a certain point cloud quality). Different heuristic algorithms were utilized to find near-optimal solutions, such as simulated annealing, greedy search, genetic algorithm, particle swarm optimization, and ant colony optimization methods [45][46][47][48]. All studies reduced the problem to 2D due to the high numerical complexity.…”

Section: Optimization Of Geodetic Networkmentioning

confidence: 99%

Designing and Evaluating a User-Oriented Calibration Field for the Target-Based Self-Calibration of Panoramic Terrestrial Laser Scanners

2019

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Calibration of terrestrial laser scanners (TLSs) is one of the fundamental tasks for assuring the high measurement accuracy required by an increasing number of end-users. Nevertheless, the development of user-oriented calibration approaches is still an active topic of research. The calibration fields for the target-based self-calibration of TLSs described in the literature are based on the quasi-random distribution of a high number of targets, and they rely on heavy redundancy. This redundancy assures highly accurate calibration results, however, with the price of reduced efficiency. In contrast, this work follows the design, implementation, and validation of a user-oriented, cost-efficient calibration field intended for TLS calibration prior to measurement campaigns. Multiple goals and constraints are placed upon the design of the calibration field, such as comprehensive calibration for high-end panoramic TLSs considering all relevant mechanical misalignments, delivering stable and reusable calibration parameters, increasing calibration efficiency by minimizing calibration-field assembly, measurement acquisition and processing time through reducing the number of targets and scanner stations, as well as estimating calibration parameters with predefined quality criteria. The calibration field design was derived through a series of simulation experiments and it was compared with the current state of the art. The simulations indicate comparable calibration results, with eight times smaller number of targets (14 instead of 120). The implemented calibration field was tested on a range of instruments, successfully improving the measurement quality, both in situ and in the subsequent applications.

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“…To compare, multiple studies were published, focusing on the viewpoint planning of optimal TLS stations with respect to the measured object. These studies tend to simplify the problem in order to reduce computational complexity, mostly by reducing the whole problem from 3D to 2D [18][19][20]. However, such an approach is not suited for TLS calibration since the vertical position of the targets is of high importance.…”

Section: Introductionmentioning

confidence: 99%

Sensitivity Analysis and Minimal Measurement Geometry for the Target-Based Calibration of High-End Panoramic Terrestrial Laser Scanners

2019

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Target-based calibration is the most widely used calibration procedure for terrestrial laser scanners. Nevertheless, the current implementations of this procedure are suboptimal due to the high complexity of their calibration fields. Therefore, designing calibration fields, by selecting optimal target locations with respect to the instrument, is a pending research question. Answering this question would lead to a more efficient and user-friendly calibration, as well as improved calibration results. Herein, we conduct a detailed analysis of the most sensitive target-to-instrument measurement configuration to estimate individual calibration parameters. Based on this analysis, we derive a minimal measurement geometry, sensitive to all mechanical misalignments, relevant for high-end panoramic terrestrial laser scanners. We support our sensitivity analysis by a simulation and empirical experiments followed by a discussion about the advantages and shortcomings of the proposed minimal measurement geometry.

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A Comparison of Simulated Annealing, Genetic Algorithm and Particle Swarm Optimization in Optimal First-Order Design of Indoor TLS Networks

Cited by 45 publications

References 32 publications

Ensemble cluster pruning via convex‐concave programming

Ensemble cluster pruning via convex‐concave programming

Designing and Evaluating a User-Oriented Calibration Field for the Target-Based Self-Calibration of Panoramic Terrestrial Laser Scanners

Sensitivity Analysis and Minimal Measurement Geometry for the Target-Based Calibration of High-End Panoramic Terrestrial Laser Scanners

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