Estimation Of Distribution Algorithm Research Articles

Research on Distributed Cooperative Intelligent Spectrum Sensing of UAV Cluster

Spectrum sensing is important to improving the survivability of unmanned aerial vehicles (UAVs) in complex electromagnetic environments. At a low signal-to-noise ratio (SNR), a UAV cluster has more prominent advantages in cluster distributed cooperative sensing than a single UAV. Aiming at this urgent need, joint optimal design is carried out for adaptive spectrum sensing algorithm and distributed estimation algorithm to realize the distributed cooperative intelligent spectrum sensing of UAV cluster. In this paper, the adaptive theory is analyzed first, and the performance of the conventional energy-aware sensing method and the least mean square (LMS) spectrum sensing algorithm is compared. In a complex electromagnetic environment, it is proposed to replan the real-time network by deleting erroneous data nodes in order to eliminate parameter estimation deviations caused by data errors. Under the condition of ensuring detection probability, the fast spectrum sensing algorithm based on SNR estimation is optimized by adaptively selecting and setting the SNR threshold to solve the problem of complex and slow calculation. The superiority of distributed spectrum estimation algorithm without erroneous data nodes is verified at a low SNR, showing that the algorithm has a good steady-state error curve and avoids the impact of data errors on detection results. In addition, the effectiveness of optimizing the fast spectrum sensing algorithm by selecting and setting the SNR threshold is verified to improve the distributed cooperative intelligent spectrum sensing rate of UAV cluster.

An efficient mixture sampling model for gaussian estimation of distribution algorithm

Estimation of distribution algorithm (EDA) is a stochastic optimization algorithm based on probability distribution model and has been widely applied in global optimization. However, the random sampling of Gaussian EDA (GEDA) usually suffers from the poor diversity and the premature convergence, which severely limits its performance. This paper analyzes the shortcomings of the random sampling and develops an efficient mixture sampling model (EMSM). EMSM can explore more promising regions and utilize the unsuccessful mutation vectors, which achieves a good tradeoff between the diversity and the convergence. Moreover, the feasibility analysis of EMSM is studied. A new GEDA variant named EMSM-EDA is developed, which combines EMSM with enhancing Gaussian estimation of distribution algorithm (EDA2). The experimental results on IEEE CEC2013 and IEEE CEC2014 test suites demonstrate that EMSM-EDA is efficient and competitive.

Estimation of distribution algorithms using Gaussian Bayesian networks to solve industrial optimization problems constrained by environment variables

Many real-world optimization problems involve two different subsets of variables: decision variables, and those variables which are not present in the cost function but constrain the solutions, and thus, must be considered during optimization. Thus, dependencies between and within both subsets of variables must be considered. In this paper, an estimation of distribution algorithm (EDA) is implemented to solve this type of complex optimization problems. A Gaussian Bayesian network is used to build an abstraction model of the search space in each iteration to identify patterns among the variables. As the algorithm is initialized from data, we introduce a new hyper-parameter to control the influence of the initial data in the decisions made during the EDA execution. The results show that our algorithm improves the cost function more than the expert knowledge does.

Secure Distributed Estimation Against Data Integrity Attacks in Internet-of-Things Systems

A secure distributed estimation method against data integrity attacks is presented for general Internet-of-Things (IoT) systems. By exploiting the spatial sparsity of the attacks, a robust optimal estimation objective to protect the entire IoT system is established. Then, a false data processor is developed to suppress the bad effects of the attacks. The convergence of the presented method is analyzed. It shows that, under accessible conditions, the estimation error will converge to be uniformly bounded for all cases, i.e., all communication links and transmitted data are arbitrarily compromised. Furthermore, by utilizing the property of persistence, a quantitative trust model is established for IoT systems based on the past behaviors of the nodes. Then, a trust-based distributed estimation method is proposed to enhance security. Simulation results by considering an IoT system with 50 nodes and 145 edges are also provided to verify our theoretical results. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Note to Practitioners</i> —The increasingly serious attack events that happened in recent years demonstrate the importance of security in the development of Internet-of-Things (IoT) systems. Different from the traditional Internet, IoT systems are usually constrained by computing and storage resources. Then, complex encryption and authentication algorithms or protocols will not be applicable to IoT systems. Distributed estimation plays an important role in the collaborative control, monitoring, and management of IoT systems. Inevitably, attacks to distributed estimation could make false data spread throughout the networks and further cause terrible consequences, while the issues of network security have not been yet taken into consideration in the existing design of distributed estimation. In this article, we propose a secure distributed estimation method. By adding a security mechanism into the distributed estimation algorithm, the ability of resistance to any data integrity attacks can be achieved. Moreover, the possible damage will be completely controllable for arbitrary attacks. That is, our method can guarantee that the estimation performance will always be acceptable even in the worst case. Besides, it is simple and lightweight, and no additional communication consumption and hardware devices are needed for its deployment and operations.

A Simple Greedy Heuristic for Site Specific Management Zone Problem

In agriculture, the soil properties influence the productivity and quality of crops. The farmer expects that in a specific area of the land, the physicochemical characteristics of the soil will be homogeneous as the selected crop has the desired quality and minimizes the use of fertilizers. There are three approaches to determining the correct delimitation of the land in the state-of-the art. The first one (k-means and fuzzy k-means) is impractical for current agricultural technology. The second approach is based on integer linear programming and a pre-processing step. This approach limits the shapes of delimited zones to rectangular, and the third approach extends the solution search space and generates orthogonal regions using Estimation of Distribution Algorithms (EDA). In this work, we generate orthogonal regions with a different approach to the EDA, a greedy construction heuristic. Our heuristic produces feasible solutions with a reasonable running time compared with the running times of EDA.

An estimation of distribution algorithm with multiple intensification strategies for two-stage hybrid flow-shop scheduling problem with sequence-dependent setup time

An estimation of distribution algorithm with multiple intensification strategies for two-stage hybrid flow-shop scheduling problem with sequence-dependent setup time

WCCI/GECCO 2020 Competition on Evolutionary Computation in the Energy Domain: An overview from the winner perspective

Evolutionary computation is attracting attention in the energy domain as an alternative to tackle inherent mathematical complexity of some problems related to high-dimensionality, non-linearity, non-convexity, multimodality, or discontinuity of the search space. In this context, the research community launched the 2020 ”Competition on Evolutionary Computation in the Energy Domain: Smart Grid Applications” and an associated simulation framework to evaluate the performance of state-of-the-art evolutionary algorithms solving real-world problems. The competition includes two testbeds: (1) Day-ahead energy resource management problem in smart grids under uncertain environments; and (2) Bi-level optimization of end-users’ bidding strategies in local energy markets. This paper describes the general framework of the competition, the two testbeds, and the evolutionary algorithms that participated. A special section is dedicated to the winner approach, CUMDANCauchy++, a cellular Estimation Distribution Algorithm (EDA). A thorough analysis of the results reveals that, led by CUMDANCauchy++, the top three algorithms form a block of approaches all based on cellular EDAs. Moreover, for testbed 2, in which CUMDANCauchy++ did not achieve the best performance, the winner approach is also based on EDAs. The outcomes of the competition show that CUMDANCauchy++ is an effective algorithm solving both testbeds, and EDAs emerge as an algorithm class with promising performance for solving smart grid applications.

An enhanced two phase estimation of distribution algorithm for solving scheduling problem

ABSTRACT Scheduling is one critical issue both in the field of industry engineering and combinatorial optimization research. In order to solve multi-objective scheduling problem with uncertainty, this paper presents a method of enhanced hybrid Estimation of Distribution Algorithm (EDA) with Teaching and Learning-Based Optimization Algorithm (TLBO). First, in order to concentrate their respective advantages, two algorithms of EDA and TLBO are integrated to enhance the capability of both global and local search. Second, scenario-based simulation is adopted to deal with uncertainty, and an adaptive sampling strategy is involved to dynamically adjust the number of scenarios during the evolving process. Third, a problem-specific local search is designed to further improve the optimality of candidate solutions. By comparing with existing algorithms on the benchmark problems of flexible job shop scheduling problem (FJSP), it is to demonstrate that our proposal can obtain better solutions in the aspects of optimality and computational efficiency.

Fully Distributed Filtering With a Stochastic Event-Triggered Mechanism

This article focuses on distributed filtering for a discrete time-varying system observed by multiple smart sensors, where every sensor only measures partial state information of the target system and then sends it to a corresponding remote estimator. Subsequently, the estimator performs the local Kalman filter and shares its estimates with the estimators in its neighborhood in a distributed way. This article aims to reduce the communication rate between sensors and estimators, and guarantee the estimation performance, simultaneously. To achieve this goal, a novel distributed information fusion algorithm is designed by embedding a stochastic event-triggered communication mechanism. Based on a new developed mathematics technique, the consistency and stability of the proposed distributed state estimation algorithms are both ensured. Furthermore, compared with the literature, the stability can be guaranteed with a milder collectively uniformly observable condition. Moreover, the tradeoff between the communication rate and estimation performance is analyzed in a closed-form expression. Finally, the effectiveness of the theoretical results is demonstrated by several comparative numerical examples.

Bearing Fault Classification Using Improved Antlion Optimizer and Extreme Learning Machine

Bearing is an important part of rotating machinery, and its early fault diagnosis and accurate classification have always been difficult in engineering application. At present, the models based on the fusion of various optimization algorithms and neural networks have become one of the emerging techniques for accurate fault identification. Firstly, an improved antlion optimizer (ALO) algorithm based on estimation of distribution algorithm (EDA) and variable-step Lévy flight strategy, abbreviated as ELALO, is proposed as a new bionic intelligence. During the initialization of population, the individuals with poor fitness are redistributed by the Gaussian probability model. In view of the stagnation of iteration, Lévy flight strategy is introduced and the adaptive change of disturbance step length is controlled. Experimental results on 4 benchmark functions show that the novel ELALO can effectively improve the solution accuracy and convergence speed, compared with the original ALO. Secondly, in order to solve the disadvantage that extreme learning machine (ELM) network is easy to fall into local optimization, this ELALO algorithm is used to initialize the weights and thresholds of its network and to form the new pattern recognition model, ELALO-ELM. Finally, the bearing data of 8 patterns from Western Reserve University are decomposed by local mean decomposition (LMD), and then the symbolic entropy (SE) of the first three product function (PF) components signals is extracted and used as the input eigenvectors. Compared with the standard ELM and ALO-ELM models, the ELALO-ELM model has better generalization and stronger robustness and it can effectively improve the efficiency of network training and the accuracy of early fault pattern classification in bearing fault diagnosis. The new ELALO-ELM model can also be used for other difficult classification problems.

Distributed Error Estimation for Quasi-Synchronization of Heterogeneous Dynamical Networks

This article proposes a distributed error estimation approach to investigate the quasi-synchronization problem of heterogeneous dynamical networks with static and adaptive coupling laws under a directed communication topology. By introducing a pining control strategy, a novel class of distributed error estimation algorithms is proposed for solving the quasi-synchronization problem of heterogeneous dynamical networks with a static coupling law by exploring the local information among the neighboring nodes. By removing the assumption that the smallest real part of the nonzero eigenvalues of the Laplacian matrix associated with the communication graph must be known, we also address the case of the quasi-synchronization of heterogeneous dynamical networks with nonidentical nonlinear dynamics and an adaptive coupling law. It is proved, in the sense of Lyapunov function, that the distributed error estimation for the quasi-synchronization problem of heterogeneous dynamical networks with static and adaptive coupling laws will be achieved with a bounded synchronization error level. Finally, two examples are presented to demonstrate the effectiveness of the proposed approaches.

A hybrid imperialist competitive algorithm for the outpatient scheduling problem with switching and preparation times

During recent years, the outpatient scheduling problem has attracted much attention from both academic and medical fields. This paper considers the outpatient scheduling problem as an extension of the flexible job shop scheduling problem (FJSP), where each patient is considered as one job. Two realistic constraints, i.e., switching and preparation times of patients are considered simultaneously. To solve the outpatient scheduling problem, a hybrid imperialist competitive algorithm (HICA) is proposed. In the proposed algorithm, first, the mutation strategy with different mutation probabilities is utilized to generate feasible and efficient solutions. Then, the diversified assimilation strategy is developed. The enhanced global search heuristic, which includes the simulated annealing (SA) algorithm and estimation of distribution algorithm (EDA), is adopted in the assimilation strategy to improve the global search ability of the algorithm.?Moreover, four kinds of neighborhood search strategies are introduced to?generate new?promising?solutions.?Finally, the empires invasion strategy?is?proposed to?increase the diversity of the population. To verify the performance of the proposed HICA, four efficient algorithms, including imperialist competitive algorithm, improved genetic algorithm, EDA, and modified artificial immune algorithm, are selected for detailed comparisons. The simulation results confirm that the proposed algorithm can solve the outpatient scheduling problem with high efficiency.

Event-Triggered Distributed Estimation with Decaying Communication Rate

We study distributed estimation of a high-dimensional static parameter vector through a group of sensors whose communication network is modeled by a fixed directed graph. Different from existing time-triggered communication schemes, an event-triggered asynchronous scheme is investigated in order to reduce communication while preserving estimation convergence. A distributed estimation algorithm with a single step size is first proposed based on an event-triggered communication scheme with a time-dependent decaying threshold. With the event-triggered scheme, each sensor sends its estimate to neighbor sensors only when the difference between the current estimate and the last sent-out estimate is larger than the triggering threshold. We prove that the proposed algorithm has mean-square and almost-sure convergence respectively, under an integrated condition of sensor network topology and sensor measurement matrices. The condition is satisfied if the topology is a balanced digraph containing a spanning tree and the system is collectively observable. Moreover, we provide estimates for the convergence rates, which are related to the step size as well as the triggering threshold. Furthermore, as an essential metric of sensor communication intensity in the event-triggered distributed algorithms, the communication rate is proved to decay to zero with a certain speed almost surely as time goes to infinity. We show that given the step size, adjusting the decay speed of the triggering threshold can lead to a tradeoff between the convergence rate of the estimation error and the decay speed of the communication rate. Specifically, increasing the decay speed of the threshold would make the communication rate decay faster, but reduce the convergence rate of the estimation error. Numerical simulations are provided to illustrate the developed results.

UAV path optimization with an integrated cost assessment model considering third-party risks in metropolitan environments

Various applications of unmanned aerial vehicles (UAVs) in urban environments facilitate our daily life and public services. However, UAV operations bring third party risk (TPR) issues, as UAV may crash to pedestrians and vehicles on the ground. It may also cause property damages to critical infrastructures and noise impacts to the public. Path planning is an effective method to mitigate these risks and impacts by avoiding high-risk areas before flight. However, most of the existing path planning methods focus on minimizing flight distance or energy cost, rarely considered risk cost. This paper develops a novel flight path optimization method that considers an integrated cost assessment model. The assessment model incorporates fatality risk, property damage risk, and noise impact, which is an extension of current TPR indicators at modeling and assessment levels. To solve the proposed integrated cost-based path optimization problem, a hybrid estimation of distribution algorithm (EDA) and CostA* (named as EDA-CostA*) algorithm is proposed, which provides both global and local heuristic information for path searching in cost-based environments. A downtown area in Singapore is selected for the case study. Simulation results demonstrate the effectiveness of the developed cost-based path optimization model in reducing the risk cost. The statistical analysis for 100 sampled environments also shows the reliability of the proposed method, which reduced the cost by [42.64%, 44.15%] at 95% confidence level.

Distributed Estimation with Decentralized Control for Quadruple-Tank Process

This paper proposes the design of quadruple-tank process due to the unique multivariable MIMO system under minimum and non-minimum scenario with respect to the valve ratio. This model is then implemented the distributed estimation algorithm with decentralized control. The inputs are set in divergent gains of pumps while the four-tank process is interconnected so that the stability properties are different, making the usage of decentralized control is reasonable. The number of outputs is designed the same as those of inputs which are also that of distributed Luenberger observer with the continuous linearized dynamical system. This distributed comprises local estimates only in certain output, meaning that it would lead to insufficiency so that the neighbouring links under some network topologies are required in the dynamical system. This concept fortunately works in two different characteristic stability of the tank process regarding estimating the states. This success leads to the further research of the more large-scale dynamic complex system.

Reliability evaluation of a composite power system in the presence of renewable generations

Power system reliability evaluation plays a vital role in the planning and operation studies by reflecting the system safety level. In this paper, a combination of a non-sequential Monte Carlo simulation (MCS)-based model and an improved Estimation of Distribution Algorithm (EDA) is exploited for evaluating the reliability of the composite power systems considering variability and uncertainty of wind farms (WFs) and Photovoltaic (PV) units. Variability of these resources is defined as the random fluctuation of wind speed and solar irradiation caused by changes in the atmosphere, while their uncertainty results from output power forecast errors. In the proposed model, the states of traditional generating units, transmission lines, WFs, and PV units are constructed using non-sequential MCS. These states can be achieved based on the component failure probability for dispatchable traditional generators and transmission lines along with the Probability Distribution Functions (PDFs) of renewable generations. To enhance the computational efficiency of the MCS in the sampling step, the improved EDA upgraded with the Population-Based Incremental Learning (PBIL) algorithm is employed. The proposed mathematical model for reliability evaluation of composite power system is applied to the IEEE RTS 24-bus system, and numerical studies are performed under several case studies. The simulation results confirm the proficiency of the proposed method to improve the computational efficiency, while the high accuracy of reliability evaluation of the composite power system is attained.

Distributed Nash equilibrium seeking design for energy consumption games of HVAC systems over digraphs

The energy consumption problem of heating, ventilation, and air conditioning systems over general directed graphs is investigated. The considered problem is firstly reformulated as a Nash equilibrium seeking problem, and a distributed consensus-based algorithm is then proposed to solve it. To address the challenge arising from general directed graphs, a distributed estimation algorithm is embedded such that the explicit dependence on the left eigenvector associated with the eigenvalue zero of the Laplacian matrix can be avoided. Then, the exponential convergence of the proposed distributed Nash equilibrium seeking algorithm is established under a standing assumption. A numerical example is finally provided to verify the effectiveness of the proposed algorithm.

Distributed Control-Estimation Synthesis for Stochastic Multi-Agent Systems via Virtual Interaction Between Non-Neighboring Agents

This paper considers the optimal distributed control problem for a linear stochastic multi-agent system (MAS). Due to the distributed nature of MAS network, the information available to an individual agent is limited to its vicinity. From the entire MAS aspect, this imposes the structural constraint on the control law, making the optimal control law computationally intractable. This paper attempts to relax such a structural constraint by expanding the neighboring information for each agent to the entire MAS, enabled by the distributed estimation algorithm embedded in each agent. By exploiting the estimated information, each agent is not limited to interact with its neighborhood but further establishing the `virtual interactions' with the non-neighboring agents. Then the optimal distributed MAS control problem is cast as a synthesized control-estimation problem. An iterative optimization procedure is developed to find the control-estimation law, minimizing the global objective cost of MAS.

Quantized Distributed Estimation With Adaptive Combiner

Distributed estimation over networks has attracted great attention for its wide applicability in source tracking, environmental monitoring, etc. In the problem of distributed estimation, a set of nodes collectively estimates some parameter from noisy measurements. Most distributed algorithms assume that the information shared among nodes has full-precision. However, this assumption is unrealistic as communication channels among nodes have limited bandwidth. Considering this, in this paper, we impose quantization constraints on distributed estimation problem. That is, nodes can only receive quantized information from neighbors. The quantization inevitably brings in errors. Each node has to decide carefully which information provided by the neighbors is more trustworthy since these information could suffer from various degrees of distortion caused by the errors. To make good use of the aggregated information, we propose an adaptive combination strategy for quantized distributed estimation algorithms. We derive sufficient conditions for convergence of the resulting algorithm through mean-square analysis. The advantages of the proposed adaptive combiner over fixed combiners are illustrated experimentally.

Event-Based Control for Synchronization of Stochastic Linear Systems with Application to Distributed Estimation

This paper studies the synchronization of stochastic linear systems which are subject to a general class of noises, in the sense that the noises are bounded in covariance but might be correlated with the states of agents and among each other. We propose an event-based control protocol for achieving the synchronization among agents in the mean square sense and theoretically analyze the performance of it by using a stochastic Lyapunov function, where the stability of c-martingales is particularly developed to handle the challenges brought by the general model of noises and the event-triggering mechanism. The proposed event-based synchronization algorithm is then applied to solve the problem of distributed estimation in sensor network. Specifically, by losslessly decomposing the optimal Kalman filter, it is shown that the problem of distributed estimation can be resolved by using the algorithms designed for achieving the synchronization of stochastic linear systems. As such, an event-based distributed estimation algorithm is developed, where each sensor performs local filtering solely using its own measurement, together with the proposed event-based synchronization algorithm to fuse the local estimates of neighboring nodes. With the reduced communication frequency, the designed estimator is proved to be stable under the minimal requirements of network connectivity and collective system observability.