scholarly journals Research on Multi-UAV Collaborative Search in Dynamic Environment

2018 ◽  
Vol 173 ◽  
pp. 02002
Author(s):  
ZHAN Jia ◽  
XIE Wenjun ◽  
GUO Qing
Keyword(s):  
Random Search ◽  
Dynamic Environment ◽  
Experimental Result ◽  
Scheduling Problem ◽  
Problem Model ◽  
Bee Colony ◽  
Collaborative Search ◽  
Point Operator ◽  
Dual Evolution ◽  
Multi Uav

Combine the uncertainty of dynamic targets, a multi-UAV reconnaissance scheduling problem model was constructed under dynamic environment, take advantage of the characteristics of dual evolution, the insertion point operator and reverse sequence operator are improved, and the problem is solved by the artificial bee colony algorithm based on the semi-random search strategy. Finally, the simulation experiment was done in the background of South China Sea, and the experimental result shows the effectiveness and feasibility of the proposed algorithm for solving the multi-UAV reconnaissance scheduling problem.

A quasi-affine transformation artificial bee colony algorithm for global optimization

2021 ◽  
pp. 1-18
Author(s):  
Baohua Zhao ◽  
Tien-Wen Sung ◽  
Xin Zhang
Keyword(s):  
Affine Transformation ◽  
Artificial Bee Colony ◽  
Triangular Matrix ◽  
Convergence Speed ◽  
Test Set ◽  
Abc Algorithm ◽  
Bee Colony ◽  
Search Mechanism ◽  
Multiple Dimensions ◽  
Collaborative Search

The artificial bee colony (ABC) algorithm is one of the classical bioinspired swarm-based intelligence algorithms that has strong search ability, because of its special search mechanism, but its development ability is slightly insufficient and its convergence speed is slow. In view of its weak development ability and slow convergence speed, this paper proposes the QABC algorithm in which a new search equation is based on the idea of quasi-affine transformation, which greatly improves the cooperative ability between particles and enhances its exploitability. During the process of location updating, the convergence speed is accelerated by updating multiple dimensions instead of one dimension. Finally, in the overall search framework, a collaborative search matrix is introduced to update the position of particles. The collaborative search matrix is transformed from the lower triangular matrix, which not only ensures the randomness of the search, but also ensures its balance and integrity. To evaluate the performance of the QABC algorithm, CEC2013 test set and CEC2014 test set are used in the experiment. After comparing with the conventional ABC algorithm and some famous ABC variants, QABC algorithm is proved to be superior in efficiency, development ability, and robustness.

scholarly journals A Modified Artificial Bee Colony for the Non-Smooth Dynamic Economic/Environmental Dispatch

2018 ◽  
Vol 8 (5) ◽  
pp. 3321-3328 ◽  
Author(s):  
Ι. Marouani ◽  
A. Boudjemline ◽  
T. Guesmi ◽  
H. H. Abdallah
Keyword(s):  
Artificial Bee Colony ◽  
Random Search ◽  
Line Planning ◽  
Bee Colony ◽  
Search Mechanism ◽  
Loading Effects ◽  
On Line ◽  
Explosion Method ◽  
Metaheuristic Techniques ◽  
Emission Dispatch

This paper presents an improved artificial bee colony (ABC) technique for solving the dynamic economic emission dispatch (DEED) problem. Ramp rate limits, valve-point loading effects and prohibited operating zones (POZs) have been considered. The proposed technique integrates the grenade explosion method and Cauchy operator in the original ABC algorithm, to avoid random search mechanism. However, the DEED is a multi-objective optimization problem with two conflicting criteria which need to be minimized simultaneously. Thus, it is recommended to provide the best solution for the decision-makers. Shannon’s entropy-based method is used for the first time within the context of the on-line planning of generator outputs to extract the best compromise solution among the Pareto set. The robustness of the proposed technique is verified on six-unit and ten-unit system tests. Results proved that the proposed algorithm gives better optimum solutions in comparison with more than ten metaheuristic techniques.

scholarly journals Earthquake Detection in a Static and Dynamic Environment Using Supervised Machine Learning and a Novel Feature Extraction Method

Sensors ◽  
2020 ◽  
Vol 20 (3) ◽  
pp. 800 ◽  
Author(s):  
Irshad Khan ◽  
Seonhwa Choi ◽  
Young-Woo Kwon
Keyword(s):  
Machine Learning ◽  
Real Time ◽  
Dynamic Environment ◽  
Supervised Machine Learning ◽  
Experimental Result ◽  
Feature Extraction Method ◽  
Proposed Model ◽  
Unseen Data ◽  
Iot Devices ◽  
Hard Real Time

Detecting earthquakes using smartphones or IoT devices in real-time is an arduous and challenging task, not only because it is constrained with the hard real-time issue but also due to the similarity of earthquake signals and the non-earthquake signals (i.e., noise or other activities). Moreover, the variety of human activities also makes it more difficult when a smartphone is used as an earthquake detecting sensor. To that end, in this article, we leverage a machine learning technique with earthquake features rather than traditional seismic methods. First, we split the detection task into two categories including static environment and dynamic environment. Then, we experimentally evaluate different features and propose the most appropriate machine learning model and features for the static environment to tackle the issue of noisy components and detect earthquakes in real-time with less false alarm rates. The experimental result of the proposed model shows promising results not only on the given dataset but also on the unseen data pointing to the generalization characteristics of the model. Finally, we demonstrate that the proposed model can be also used in the dynamic environment if it is trained with different dataset.

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