scholarly journals An Efficient Distributed Area Division Method for Cooperative Monitoring Applications with Multiple UAVs

Sensors ◽  
2020 ◽  
Vol 20 (12) ◽  
pp. 3448 ◽  
Author(s):  
José Joaquín Acevedo ◽  
Ivan Maza ◽  
Anibal Ollero ◽  
Begoña C. Arrue
Keyword(s):  
Online Algorithm ◽  
Optimal Solution ◽  
Convergence Time ◽  
Division Problem ◽  
Size And Shape ◽  
Multiple Uavs ◽  
Monitoring Applications ◽  
Neighborhood Relation ◽  
Optimal Area ◽  
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This article addresses the area division problem in a distributed manner providing a solution for cooperative monitoring missions with multiple UAVs. Starting from a sub-optimal area division, a distributed online algorithm is presented to accelerate the convergence of the system to the optimal solution, following a frequency-based approach. Based on the “coordination variables” concept and on a strict neighborhood relation to share information (left, right, above and below neighbors), this technique defines a distributed division protocol to determine coherently the size and shape of the sub-area assigned to each UAV. Theoretically, the convergence time of the proposed solution depends linearly on the number of UAVs. Validation results, comparing the proposed approach with other distributed techniques, are provided to evaluate and analyze its performance following a convergence time criterion.

scholarly journals Online Clique Clustering

Algorithmica ◽  
2019 ◽  
Vol 82 (4) ◽  
pp. 938-965
Author(s):  
Marek Chrobak ◽  
Christoph Dürr ◽  
Aleksander Fabijan ◽  
Bengt J. Nilsson
Keyword(s):  
Objective Function ◽  
Competitive Ratio ◽  
Online Algorithm ◽  
Optimal Solution ◽  
Deterministic Algorithm ◽  
Input Graph ◽  
Online Clustering ◽  
Objective Value ◽  
Asymptotic Competitive Ratio ◽  
Better Than

Abstract Clique clustering is the problem of partitioning the vertices of a graph into disjoint clusters, where each cluster forms a clique in the graph, while optimizing some objective function. In online clustering, the input graph is given one vertex at a time, and any vertices that have previously been clustered together are not allowed to be separated. The goal is to maintain a clustering with an objective value close to the optimal solution. For the variant where we want to maximize the number of edges in the clusters, we propose an online algorithm based on the doubling technique. It has an asymptotic competitive ratio at most 15.646 and a strict competitive ratio at most 22.641. We also show that no deterministic algorithm can have an asymptotic competitive ratio better than 6. For the variant where we want to minimize the number of edges between clusters, we show that the deterministic competitive ratio of the problem is $$n-\omega (1)$$n-ω(1), where n is the number of vertices in the graph.

Optimal Solving of the Contradictory Problem between Hydraulic Cylinder's Precision and Stability with Extenics Theory

2013 ◽  
Vol 436 ◽  
pp. 518-530 ◽  
Author(s):  
Adrian Olaru
Keyword(s):  
Dynamic Behavior ◽  
Transfer Functions ◽  
Optimal Solution ◽  
Hydraulic Cylinder ◽  
Gradient Force ◽  
Maximal Acceleration ◽  
Flow Loss ◽  
On Line ◽  
Optimal Values ◽  
Optimal Area

In the optimisation stage of the systems one of the more important step is the optimisation of the dynamic behavior of all elements of the system, with priority the elements what have the slow frequency, like motors. The paper try to show how will be possible to optimise very easily the dynamic behavior of elements and systems, using LabVIEW propre instrumentation, the transfer functions theory and the Extenics Theory to solve the contradictory problems. By appling the virtual LabVIEW instrumentation will be possible to choose on-line the optimal values for each constructive and functional parameters of the elements and the systems to obtain one good dynamic answer: maximal acceleration without vibration, minimum answer time and maximal precision. In the paper was defined the optimal area of the precision-stability by imposed the breaking frequency from the Bode characteristics to optain the desired acceleration time. The paper shown the desired constraints and by using the Extenics theory was possible to choose the optimal solution of the precision-stability contradictory problem. In the research were used some different virtual LabVIEW instruments, to simulate the dynamic behavior of the cylinder when the active area, flow loss gradient, force gradient were changed in his desired physical field. By using the assisted research and the Extenics theory was possible to find the optimal values for the dynamic parameters and to be sure that working point will be inside of the desirable field of the hydraulic cylinder precision-stability.

scholarly journals Demand Side Energy Management via Multiagent Coordination in Consumer Cooperatives

2014 ◽  
Vol 50 ◽  
pp. 885-922 ◽  
Author(s):  
A. Veit ◽  
Y. Xu ◽  
R. Zheng ◽  
N. Chakraborty ◽  
K. Sycara
Keyword(s):  
Cost Reduction ◽  
Energy Demand ◽  
Incentive Compatibility ◽  
Electric Energy ◽  
Optimal Solution ◽  
Convergence Time ◽  
Multiagent Coordination ◽  
Price Functions ◽  
The Cost ◽  
The Coordinator

A key challenge in creating a sustainable and energy-efficient society is to make consumer demand adaptive to the supply of energy, especially to the renewable supply. In this article, we propose a partially-centralized organization of consumers (or agents), namely, a consumer cooperative that purchases electricity from the market. In the cooperative, a central coordinator buys the electricity for the whole group. The technical challenge is that consumers make their own demand decisions, based on their private demand constraints and preferences, which they do not share with the coordinator or other agents. We propose a novel multiagent coordination algorithm, to shape the energy demand of the cooperative. To coordinate individual consumers under incomplete information, the coordinator determines virtual price signals that it sends to the consumers to induce them to shift their demands when required. We prove that this algorithm converges to the central optimal solution and minimizes the electric energy cost of the cooperative. Additionally, we present results on the time complexity of the iterative algorithm and its implications for agents' incentive compatibility. Furthermore, we perform simulations based on real world consumption data to (a) characterize the convergence properties of our algorithm and (b) understand the effect of differing demand characteristics of participants as well as of different price functions on the cost reduction. The results show that the convergence time scales linearly with the agent population size and length of the optimization horizon. Finally, we observe that as participants' flexibility of shifting their demands increases, cost reduction increases and that the cost reduction is not sensitive to variation in consumption patterns of the consumers.

scholarly journals Jamming Strategy Optimization through Dual Q-Learning Model against Adaptive Radar

Sensors ◽  
2021 ◽  
Vol 22 (1) ◽  
pp. 145
Author(s):  
Hongdi Liu ◽  
Hongtao Zhang ◽  
Yuan He ◽  
Yong Sun
Keyword(s):  
Decision Making ◽  
Optimal Solution ◽  
Convergence Time ◽  
Mode Switching ◽  
Pulse Parameter ◽  
Q Learning ◽  
Adaptive Radar ◽  
Markov Decision ◽  
Joint Strategy ◽  
Low Dimensional

Modern adaptive radars can switch work modes to perform various missions and simultaneously use pulse parameter agility in each mode to improve survivability, which leads to a multiplicative increase in the decision-making complexity and declining performance of the existing jamming methods. In this paper, a two-level jamming decision-making framework is developed, based on which a dual Q-learning (DQL) model is proposed to optimize the jamming strategy and a dynamic method for jamming effectiveness evaluation is designed to update the model. Specifically, the jamming procedure is modeled as a finite Markov decision process. On this basis, the high-dimensional jamming action space is disassembled into two low-dimensional subspaces containing jamming mode and pulse parameters respectively, then two specialized Q-learning models with interaction are built to obtain the optimal solution. Moreover, the jamming effectiveness is evaluated through indicator vector distance measuring to acquire the feedback for the DQL model, where indicators are dynamically weighted to adapt to the environment. The experiments demonstrate the advantage of the proposed method in learning radar joint strategy of mode switching and parameter agility, shown as improving the average jamming-to-signal radio (JSR) by 4.05% while reducing the convergence time by 34.94% compared with the normal Q-learning method.

scholarly journals Estimation of optimal area and volume for double arch-dams

2018 ◽  
Vol 211 ◽  
pp. 14002 ◽  
Author(s):  
Enrico Zacchei ◽  
José Luis Molina
Keyword(s):  
Prior Distribution ◽  
Optimal Solution ◽  
Volume Estimation ◽  
Unknown Parameters ◽  
Bayesian Theorem ◽  
Arch Dams ◽  
Precise Analysis ◽  
Optimal Area ◽  
Bayesian Estimators ◽  
Area And Volume

This research is focused on the optimum area and volume estimation of double arch dams. The first stage of the methodology refers to defining issues about Bayesian estimators to obtain the value for designing the optimum dam shape. After that, the shape equations are iterated step-bystep to obtain the optimal solution. From the inventory of existing dams, it is possible to extract many important values although they are not sufficient. To obtain the non-available data, the Gaussian distribution under the Bayesian theorem hypotheses has been employed. This theorem converts the prior distribution using unknown parameters into the posterior distribution which provides expected estimators. The choice of the dam shape is strongly based on the experience, therefore by knowing and applying real information of existing dams it is possible to carry out a more precise analysis.

Short-Term Electricity Price Forecasting Based on MPSO-BP Algorithm

2012 ◽  
Vol 591-593 ◽  
pp. 1351-1355 ◽  
Author(s):  
Yu Dong ◽  
Qiang Yang ◽  
Wen Jun Yan
Keyword(s):  
Neural Network ◽  
Bp Neural Network ◽  
Electricity Market ◽  
Optimal Solution ◽  
Electricity Price ◽  
Convergence Time ◽  
Price Forecasting ◽  
Short Term ◽  
Electricity Price Forecasting ◽  
Improved Performance

In this paper, we exploited the short-term electricity price forecasting issue by introducing a global search mechanism based on the improved particle swarm optimization (MPSO) algorithm for the neural network training. The proposed MPSO algorithm is used for the initial weights and threshold of BP neural network in the process of optimization. We then proposed a novel short-term electricity price forecasting model based on MPSO-BP neural network. The paper provides a number of examples of bidding model of the California electricity market to forecasting market clear price using BP neural network trained by MPSO. Through the comparative study of the conventional BP neural network and the proposed MPSO-BP neural network, the proposed method demonstrates improved performance in finding the optimal solution with excellent convergence time for all the simulated scenarios.

scholarly journals AUC Optimization with a Reject Option

2020 ◽  
Vol 34 (04) ◽  
pp. 5684-5691
Author(s):  
Song-Qing Shen ◽  
Bin-Bin Yang ◽  
Wei Gao
Keyword(s):  
Online Algorithm ◽  
Lower Cost ◽  
Convex Relaxation ◽  
Class Imbalance ◽  
Optimal Solution ◽  
Score Function ◽  
Reject Option ◽  
Erroneous Decision ◽  
Auc Optimization ◽  
Mission Critical

Making an erroneous decision may cause serious results in diverse mission-critical tasks such as medical diagnosis and bioinformatics. Previous work focuses on classification with a reject option, i.e., abstain rather than classify an instance of low confidence. Most mission-critical tasks are always accompanied with class imbalance and cost sensitivity, where AUC has been shown a preferable measure than accuracy in classification. In this work, we propose the framework of AUC optimization with a reject option, and the basic idea is to withhold the decision of ranking a pair of positive and negative instances with a lower cost, rather than mis-ranking. We obtain the Bayes optimal solution for ranking, and learn the reject function and score function for ranking, simultaneously. An online algorithm has been developed for AUC optimization with a reject option, by considering the convex relaxation and plug-in rule. We verify, both theoretically and empirically, the effectiveness of the proposed algorithm.

scholarly journals Skill acquisition and transfer through common subgoals

2019 ◽  
Vol 16 (2) ◽  
pp. 172988141983155
Author(s):  
Barış Gökçe ◽  
H Levent Akın
Keyword(s):  
Skill Acquisition ◽  
Learning Process ◽  
Substantial Reduction ◽  
Optimal Solution ◽  
Convergence Time ◽  
Social Environments ◽  
Test Bed ◽  
Main Challenge ◽  
To Come ◽  
Ending Process

The main challenge of using robots in social environments such as houses is coping with the frequent changes in tasks. Since it is infeasible to come up with an implementation for all possible cases of all tasks, robots should find solutions for new problems by themselves. So, learning is one of the major abilities for a robot to deal with changing tasks. However, it is generally time-consuming to find even a near-optimal solution for complex tasks through learning. On the other hand, learning in humans is a never-ending process and much faster, thanks to transferring prior knowledge. In this work, we build a knowledge base (called as skill library) from the subsets of the tasks discovered during the learning process. Since most of the tasks encountered have common subsets, the skill library enables us to transfer previous experiences while learning the strategy of a new task. The robot progressively accumulates skills to reduce the difficulty of learning the forthcoming tasks. We choose navigation in different unknown environments as the test bed. The results show a significant improvement especially on the performance of the robot in the initial episodes, a substantial reduction in the cost of the overall learning process, and in the convergence time to the (near-)optimal policy.

scholarly journals Biogeography-Based Optimization with Orthogonal Crossover

2013 ◽  
Vol 2013 ◽  
pp. 1-20 ◽  
Author(s):  
Quanxi Feng ◽  
Sanyang Liu ◽  
Guoqiang Tang ◽  
Longquan Yong ◽  
Jianke Zhang
Keyword(s):  
Orthogonal Design ◽  
Optimal Solution ◽  
Solution Space ◽  
Population Based ◽  
Population Diversity ◽  
Crossover Operator ◽  
Migration Operator ◽  
Migration Equation ◽  
Population Based Algorithm ◽  
Share Information

Biogeography-based optimization (BBO) is a new biogeography inspired, population-based algorithm, which mainly uses migration operator to share information among solutions. Similar to crossover operator in genetic algorithm, migration operator is a probabilistic operator and only generates the vertex of a hyperrectangle defined by the emigration and immigration vectors. Therefore, the exploration ability of BBO may be limited. Orthogonal crossover operator with quantization technique (QOX) is based on orthogonal design and can generate representative solution in solution space. In this paper, a BBO variant is presented through embedding the QOX operator in BBO algorithm. Additionally, a modified migration equation is used to improve the population diversity. Several experiments are conducted on 23 benchmark functions. Experimental results show that the proposed algorithm is capable of locating the optimal or closed-to-optimal solution. Comparisons with other variants of BBO algorithms and state-of-the-art orthogonal-based evolutionary algorithms demonstrate that our proposed algorithm possesses faster global convergence rate, high-precision solution, and stronger robustness. Finally, the analysis result of the performance of QOX indicates that QOX plays a key role in the proposed algorithm.

Online Minimum Spanning Tree with Advice

2018 ◽  
Vol 29 (04) ◽  
pp. 505-527
Author(s):  
Maria Paola Bianchi ◽  
Hans-Joachim Böckenhauer ◽  
Tatjana Brülisauer ◽  
Dennis Komm ◽  
Beatrice Palano
Keyword(s):  
Lower Bound ◽  
Weight Function ◽  
Competitive Ratio ◽  
Spanning Tree ◽  
Online Algorithm ◽  
Minimum Spanning Tree ◽  
Optimal Solution ◽  
Bounded Degree ◽  
Advice Complexity ◽  
Graph Classes

In the online minimum spanning tree problem, a graph is revealed vertex by vertex; together with every vertex, all edges to vertices that are already known are given, and an online algorithm must irrevocably choose a subset of them as a part of its solution. The advice complexity of an online problem is a means to quantify the information that needs to be extracted from the input to achieve good results. For a graph of size [Formula: see text], we show an asymptotically tight bound of [Formula: see text] on the number of advice bits to produce an optimal solution for any given graph. For particular graph classes, e.g., with bounded degree or a restricted edge weight function, we prove that the upper bound can be drastically reduced; e.g., [Formula: see text] advice bits allow to compute an optimal result if the weight function equals the Euclidean distance; if the graph is complete and has two different edge weights, even a logarithmic number suffices. Some of these results make use of the optimality of Kruskal’s algorithm for the offline setting. We also study the trade-off between the number of advice bits and the achievable competitive ratio. To this end, we perform a reduction from another online problem to obtain a linear lower bound on the advice complexity for any near-optimal solution. Using our results finally allows us to give a lower bound on the expected competitive ratio of any randomized online algorithm for the problem, even on graphs with three different edge weights.

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