Bio-inspired Computation in Unmanned Aerial Vehicles

Duan, Haibin; Li, Pei

doi:10.1007/978-3-642-41196-0

Cited by 76 publications

(33 citation statements)

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“…At any point in time, only one bee will act as a scout bee, and it will be the one to determine a new food source. Such bees are called artificial scouts [17]. Figure 4 shows the ABC mechanism, in which two discovered food sources are assumed as A and B.…”

Section: Artificial Bee Colony Algorithmmentioning

confidence: 99%

Mobile Ad Hoc Network Energy Cost Algorithm Based on Artificial Bee Colony

Tareq¹,

Alsaqour

Abdelhaq

et al. 2017

Wireless Communications and Mobile Computing

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A mobile ad hoc network (MANET) is a collection of mobile nodes that dynamically form a temporary network without using any existing network infrastructure. MANET selects a path with minimal number of intermediate nodes to reach the destination node. As the distance between each node increases, the quantity of transmission power increases. The power level of nodes affects the simplicity with which a route is constituted between a couple of nodes. This study utilizes the swarm intelligence technique through the artificial bee colony (ABC) algorithm to optimize the energy consumption in a dynamic source routing (DSR) protocol in MANET. The proposed algorithm is called bee DSR (BEEDSR). The ABC algorithm is used to identify the optimal path from the source to the destination to overcome energy problems. The performance of the BEEDSR algorithm is compared with DSR and bee-inspired protocols (BeeIP). The comparison was conducted based on average energy consumption, average throughput, average end-to-end delay, routing overhead, and packet delivery ratio performance metrics, varying the node speed and packet size. The BEEDSR algorithm is superior in performance than other protocols in terms of energy conservation and delay degradation relating to node speed and packet size.

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Section: Artificial Bee Colony Algorithmmentioning

confidence: 99%

Mobile Ad Hoc Network Energy Cost Algorithm Based on Artificial Bee Colony

Tareq¹,

Alsaqour

Abdelhaq

et al. 2017

Wireless Communications and Mobile Computing

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“…In this model, S is defined as the starting point, and T is the target point ( Figure 1) [18,19]. There are some threat resources in UCAV battlefield, for example, radars, missiles, and artillery, the effects of which all are shown in the form of a circle.…”

Section: Threat Resource Model In Ucav Path Planningmentioning

confidence: 99%

“…In this work, the threat cost of the route between two discrete points was calculated. It is approximately equal to the sum of the threat cost of five points, as shown in Figure 2 [18,19]. 1 1 (x , y ) If the i th edge is within the effect range, the calculation formula of the threat cost is as follows: …”

Section: Evaluation Functionmentioning

confidence: 99%

Quantum Wind Driven Optimization for Unmanned Combat Air Vehicle Path Planning

et al. 2015

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Unmanned combat air vehicle (UCAV) path planning aims to calculate the optimal or suboptimal flight path considering the different threats and constraints under the complex battlefield environment. This flight path can help the UCAV avoid enemy threats and improve the efficiency of the investigation. This paper presents a new quantum wind driven optimization (QWDO) for the path planning of UCAV. QWDO algorithm uses quantum rotation gate strategy on population evolution and the quantum non-gate strategy to realize the individual variation of population. These operations improve the diversity of population and avoid premature convergence. This paper tests this optimization in two instances. The experimental results show that the proposed algorithm is feasible in these two cases. Compared to quantum bat algorithm (QBA), quantum particle swarm optimization (QPSO), wind driven optimization (WDO), bat algorithm (BA), particle swarm optimization (PSO), and differential evolution (DE), the QWDO algorithm exhibited better performance. The simulation results demonstrate that the QWDO algorithm is an effective and feasible method for solving UCAV path planning.Keywords: wind driven optimization (WDO); unmanned combat air vehicle (UCAV); path planning; quantum wind driven optimization (QWDO) OPEN ACCESSAppl. Sci. 2015Sci. , 5 1458

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“…However, our proposed model requires more time to complete the training part. Time consumption is also the bottleneck for many biologically inspired models [42]. The processing speed of our model is the main limitation in real-world applications, such as the analysis of video.…”

Section: F Future Workmentioning

confidence: 99%

Biologically Inspired Model with Feature Selection for Target Recognition Using Biogeography-Based Optimization

Duan

Deng

2014

Journal of Aerospace Information Systems

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To detect salient ground targets precisely and rapidly during aerial reconnaissance, this paper describes a novel object recognition method based on the feature selection of a biologically inspired model and biogeography-based optimization. As a promising approach to object recognition, the biologically inspired model is a hierarchical system of building an increasingly complex and invariant feature representation, which closely follows the process of object recognition in the visual cortex. These scale-and position-tolerant features are constructed by alternating between a template-matching and a maximum-pooling operation. Because of the many patches extracted in the standard biologically inspired model, the random mechanism may extract patches from irrelevant parts of an image and consume a lot of time. In this work, a feature selection method is proposed based on a new population-based evolutionary algorithm called biogeography-based optimization to choose the proper set of patches with high accuracy of classification and recognition. A support vector machine classifier is used for evaluation of the fitness function in biogeography-based optimization and to calculate the recognition rate in testing. A series of experiments are conducted, and the comparative results demonstrate the feasibility and effectiveness of the approach. = size of the patch P i P i = one patch extracted at the C1 layer P s = probability of the habitat containing exact species S k = number of species w a = weight associated to the error rate w f = weight associated to the normalized summation of selected patches x = horizontal coordinate y = vertical ordinate β = sharpness of the exponential function γ = aspect ratio of Gabor filter θ = orientation of Gabor filter λ = wavelength of Gabor filter λ k = immigration rate of the kth solution μ k = emigration rate of the kth solution σ = effective width of Gabor filter

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Bio-inspired Computation in Unmanned Aerial Vehicles

Cited by 76 publications

References 0 publications

Mobile Ad Hoc Network Energy Cost Algorithm Based on Artificial Bee Colony

Mobile Ad Hoc Network Energy Cost Algorithm Based on Artificial Bee Colony

Quantum Wind Driven Optimization for Unmanned Combat Air Vehicle Path Planning

Biologically Inspired Model with Feature Selection for Target Recognition Using Biogeography-Based Optimization

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