Colliding Bodies Optimization

2014 ◽  
pp. 195-232 ◽  
Author(s):  
A. Kaveh
Keyword(s):  
Colliding Bodies Optimization

RNN-based multispectral satellite image processing for remote sensing applications

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Venkata Dasu Marri ◽  
Veera Narayana Reddy P. ◽  
Chandra Mohan Reddy S.
Keyword(s):  
Remote Sensing ◽  
Image Processing ◽  
Deep Learning ◽  
Image Classification ◽  
Satellite Image ◽  
Multispectral Image ◽  
Content Type ◽  
Colliding Bodies Optimization ◽  
Sensing Applications ◽  
Remote Sensing Applications

Purpose Image classification is a fundamental form of digital image processing in which pixels are labeled into one of the object classes present in the image. Multispectral image classification is a challenging task due to complexities associated with the images captured by satellites. Accurate image classification is highly essential in remote sensing applications. However, existing machine learning and deep learning–based classification methods could not provide desired accuracy. The purpose of this paper is to classify the objects in the satellite image with greater accuracy. Design/methodology/approach This paper proposes a deep learning-based automated method for classifying multispectral images. The central issue of this work is that data sets collected from public databases are first divided into a number of patches and their features are extracted. The features extracted from patches are then concatenated before a classification method is used to classify the objects in the image. Findings The performance of proposed modified velocity-based colliding bodies optimization method is compared with existing methods in terms of type-1 measures such as sensitivity, specificity, accuracy, net present value, F1 Score and Matthews correlation coefficient and type 2 measures such as false discovery rate and false positive rate. The statistical results obtained from the proposed method show better performance than existing methods. Originality/value In this work, multispectral image classification accuracy is improved with an optimization algorithm called modified velocity-based colliding bodies optimization.

scholarly journals Optimal placement of distributed generation using colliding bodies optimization

2018 ◽  
Vol 7 (3.3) ◽  
pp. 168
Author(s):  
Gera Kalidas Babu ◽  
P V. Ramana rao
Keyword(s):  
Distributed Generation ◽  
Optimization Algorithm ◽  
Distribution Networks ◽  
Optimal Placement ◽  
Solar Photovoltaic ◽  
Load Power ◽  
Colliding Bodies Optimization ◽  
Power Loss Reduction ◽  
Radial Distribution Networks ◽  
Distribution Generation

The present paper foremost objective is to resolve best practicable location of solar photovoltaic distribution generation (DG) of several cases using different distribution load power factors and to analyze power loss reduction. This objective achieved by a recent developed method, the so called colliding bodies’ optimization algorithm, to perceive optimum location. Performances of colliding bodies’ optimization algorithm have been evaluated and compared with other search algorithms. The execution to test viability and efficiency, the proposed collid-ing bodies’ optimization is simulated on standard IEEE 38 bus radial distribution networks. The acquired outcome from colliding bodies Optimization algorithm exhibits the possible location of distributed generation through different pre assumed load power factors compared to the other stochastic search bat and genetic algorithm.  

scholarly journals Optimization of Interplanetary Trajectories Using the Colliding Bodies Optimization Algorithm

2020 ◽  
Vol 2020 ◽  
pp. 1-16
Author(s):  
Marco Del Monte ◽  
Raffaele Meles ◽  
Christian Circi
Keyword(s):  
Optimization Algorithm ◽  
Mechanical Engineering ◽  
Initial Conditions ◽  
Metaheuristic Algorithm ◽  
Simple Formulation ◽  
Planar Case ◽  
Orbital Transfer ◽  
Colliding Bodies Optimization ◽  
The Earth ◽  
Interplanetary Trajectories

In this paper, a recent physics-based metaheuristic algorithm, the Colliding Bodies Optimization (CBO), already employed to solve problems in civil and mechanical engineering, is proposed for the optimization of interplanetary trajectories by using both indirect and direct approaches. The CBO has an extremely simple formulation and does not depend on any initial conditions. To test the performances of the algorithm, missions with remarkably different orbital transfer energies are considered: from the simple planar case, as the Earth-Mars orbital transfer, to more energetic ones, like a rendezvous with the asteroid Pallas.

scholarly journals Prof. Comparing H2 and H∞ Algorithms for Optimum Design of Tuned Mass Dampers under Near-Fault and Far-Fault Earthquake Motions

2020 ◽  
Author(s):  
Ali Kaveh ◽  
Mazyar Fahimi Fazam ◽  
Rasool Maroofiazar
Keyword(s):  
Objective Function ◽  
Optimum Design ◽  
Ground Motions ◽  
Optimization Technique ◽  
Large Set ◽  
Near Fault ◽  
Colliding Bodies Optimization ◽  
Controlled Structure ◽  
Robust Optimum Design ◽  
Far Fault

In this study, the robust optimum design of Tuned Mass Damper (TMD) is established. The H2 and H∞ norm of roof displacement transfer function are implemented and compared as the objective functions under Near-Fault (NF) and Far-Fault (FF) earthquake motions. Additionally, the consequences of different characteristics of NF ground motions such as forward-directivity and fling-step are investigated on the behavior of a benchmark 10-story controlled structure. The Colliding Bodies Optimization (CBO) is employed as an optimization technique to calculate the optimum parameters of the TMDs. The resulting statistical assessment shows that the H∞ objective function is rather superior to H2 objective function for optimum design of TMDs under NF and FF earthquake excitations. Finally, the robustness of the designed TMDs is evaluated under a large set of natural ground motions.

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