Spaceborne global navigation satellite system reflectometry (GNSS-R) techniques have been developed for sea surface wind speed retrieval in recent years. In order to utilize both the leading edge ...slope (LES) and normalized bistatic radar cross-section (NBRCS), the minimum variance estimator (MVE) is used in the cyclone GNSS (CYGNSS) algorithm for wind retrieval. However, due to the high correlation of two observables, the root mean square error (RMSE) of the MVE estimated winds is not improved significantly. In this article, a new method by combining retrievals from delay-Doppler map (DDM) observables based on particle swarm optimization (PSO) is proposed. LES and NBRCS observables from CYGNSS V2.1 products are used, and then wind retrievals from them are combined by PSO. In order to validate the performance, European Center for Medium-Range Weather Forecasts (ECMWF) and cross-calibrated multi-platform (CCMP) ocean surface wind vector analysis product 10-m ocean surface wind products are used as ground truth. The results show that, when using ECMWF winds, the RMSE of MVE retrievals is 2.21 m/s, while that of PSO is 1.95 m/s: an improvement of 12%; when using CCMP winds, the RMSE of MVE retrievals is 2.15 m/s, while that of PSO is 1.92 m/s: an improvement of 11%. Therefore, we conclude that the PSO algorithm is an improvement on the state-of-the-art MVE GNSS-R-based wind speed retrieval techniques. However, the PSO-based wind retrievals show the dependence on the GPS block type and the CYGNSS satellite identifier that the MVE-based techniques suffer from.
This paper proposes an improved version of the random drift particle swarm optimization algorithm for solving the economic dispatch problem. The improvement is achieved through adding a crossover ...operation followed by a greedy selection process while replacing the mean best position of the particles with the personal best position of each particle in the velocity updating equation. The improved algorithm is also augmented with a self-adaption mechanism that eliminates the need for tuning the algorithm parameters based on characteristics of the considered optimization problem. Practical features such as valve point effects, prohibited operating zones, multiple fuel options, and ramp rate limits are considered in the mathematical formulation of the economic dispatch problem. In order to demonstrate the efficacy of the proposed algorithm, five benchmark test systems are utilized. The obtained results showed that the improved random drift particle swarm optimization algorithm is capable of providing superior results compared to the original algorithm and the state of the art techniques proposed in previous literature.
In this article, the Nash equilibrium strategy is used to solve the multiobjective optimization problems (MOPs) with the aid of an integrated algorithm combining the particle swarm optimization (PSO) ...algorithm and the self-organizing mapping (SOM) neural network. The Nash equilibrium strategy addresses the MOPs by comparing decision variables one by one under different objectives. The randomness of the PSO algorithm gives full play to the advantages of parallel computing and improves the rate of comparison calculation. In order to avoid falling into local optimal solutions and increase the diversity of particles, a nonlinear recursive function is introduced to adjust the inertia weight, which is called the adaptive particle swarm optimization (APSO). In addition, the neighborhood relations of current particles are constructed by SOM, and the leading particles are selected from the neighborhood to guide the local and global search, so as to achieve convergence. Compared with several advanced algorithms based on the eight multiobjective standard test functions with different Pareto solution sets and Pareto front characteristics in examples, the proposed algorithm has a better performance.
In physical layer for non-orthogonal multiple access (NOMA), most existing studies focus on the non-delay-sensitive metrics such as the spectral efficiency. In order to improve user's quality of ...service (QoS) in delay-sensitive communications, however, effective capacity can be adopted to the NOMA system to consider the QoS metric while analyzing capacity. In this paper, we deduce the closed form expression for effective capacity in downlink NOMA and propose three optimization problems with delay and effective capacity constraints. Firstly, a joint rate and power allocation scheme is proposed to maximize the total effective capacity. Secondly, we deduce the optimal solution of the problem for maximizing the minimum delay QoS exponent. Thirdly, a minimum total transmit power allocation scheme is proposed with the effective capacity constraint. Since the problems of maximizing effective capacity and minimizing total transmit power are non-convex, the particle swarm optimization (PSO) algorithm is used to find global optimization solutions. Simulation results show our proposed power and rate allocation scheme maximizes the effective capacity, which is better than orthogonal multiple access (OMA). Meanwhile, the optimal minimum delay QoS exponent and minimum total transmit power with effective capacity constraint have been achieved.
A disruptive technology fundamentally transforming the way that computing services are delivered, cloud computing offers information and communication technology users a new dimension of convenience ...of resources, as services via the Internet. Because cloud provides a finite pool of virtualized on-demand resources, optimally scheduling them has become an essential and rewarding topic, where a trend of using Evolutionary Computation (EC) algorithms is emerging rapidly. Through analyzing the cloud computing architecture, this survey first presents taxonomy at two levels of scheduling cloud resources. It then paints a landscape of the scheduling problem and solutions. According to the taxonomy, a comprehensive survey of state-of-the-art approaches is presented systematically. Looking forward, challenges and potential future research directions are investigated and invited, including real-time scheduling, adaptive dynamic scheduling, large-scale scheduling, multiobjective scheduling, and distributed and parallel scheduling. At the dawn of Industry 4.0, cloud computing scheduling for cyber-physical integration with the presence of big data is also discussed. Research in this area is only in its infancy, but with the rapid fusion of information and data technology, more exciting and agenda-setting topics are likely to emerge on the horizon.
Endmember extraction (EE) is a significant task in hyperspectral unmixing. From a multiobjective optimization perspective, this task is extremely challenging because objectives often conflict with ...each other. Currently, a multiobjective discrete particle swarm optimization algorithm (MODPSO) is applied to handle the multiobjective optimization EE problem such as the root-mean-square error (RMSE) and the volume maximization (VM). However, in MODPSO, the minimization of RMSE by unconstrained least squares (Ucls) may lack accuracy, the update of velocity by the predefined random selection probability p can also affect the exploration and exploitation, and it may lose good solution in the process of the update of particles when the particles are randomly chosen in the nondominated relationship. To address these issues, we present an improved MODPSO (IMODPSO) for hyperspectral EE. IMODPSO employs nonnegative constrained least squares (Ncls) to enhance the accuracy of RMSE. Moreover, IMODPSO eliminates the effects of probability p and combines the restart mechanism to achieve a balance of the exploration and exploitation. In addition, IMODPSO utilizes the archive strategy to reserve good nondominated particles to strengthen the population diversity. The experiments have been conducted on three real hyperspectral images and the results have demonstrated that IMODPSO obtains best performances for EE.
•A logistics distribution region partitioning model is developed.•This model is to minimize the cost of two-echelon logistics distribution network.•A hybrid algorithm with PSO and GA is proposed.•The ...empirical results reveal that EPSO–GA algorithm outperforms other algorithms.
Two-echelon logistics distribution region partitioning is a critical step to optimize two or multi-echelon logistics distribution network, and it aims to assign distribution unit to a certain logistics facility (i.e. logistic center and distribution center). Given the partitioned regions, vehicle routing problem can be further developed and solved. This paper established a model to minimize the total cost of the two-echelon logistics distribution network. A hybrid algorithm named as the Extended Particle Swarm Optimization and Genetic Algorithm (EPSO–GA) is proposed to tackle the model formulation. A two-dimensional particle encoding method is adopted to generate the initial population of particles. EPSO–GA combines the merits of Particle Swarm Optimization (PSO) algorithm and Genetic Algorithm (GA) with both global and local search capability. By updating the inertia weight and exchanging best-fit solutions and worst-fit solutions between PSO and GA, EPSO–GA algorithm is able to converge to an optimal solution with a reasonable design of termination and iteration rules. The computation results from a case study in Guiyang city, China, reveal that EPSO–GA algorithm is superior to the other three algorithms, Hybrid Particle Swarm Optimization (HPSO), GA, and Ant Colony Optimization (ACO), in terms of the partitioning schemes, the total cost and number of iterations. By comparing with the exact method, the proposed approach demonstrates its capability to optimize a small scale two-echelon logistics distribution network. The proposed approach can be readily implemented in practice to assist the logistics operators reduce operational costs and improve customer service. In addition, the proposed approach is of great potential to apply in other research domains.
The advent of sensors that are light in weight, small-sized, low power and are enabled by wireless network has led to growth of wireless sensor networks (WSNs) in multiple areas of applications. The ...key problems faced in WSNs are decreased network lifetime and time delay in transmission of data. In many critical applications such as military and monitoring the eco system, disaster management, etc., data routing is very crucial. Multi hop low-energy adaptive clustering hierarchy protocol has been proposed in literature but is proved to be inefficient. Cluster head optimization is a NP hard. This paper deals with selection of optimal path in routing which improves network lifespan, as well as network’s energy efficiency. Various meta-heuristic techniques particularly particle swarm optimization (PSO) has been effectively used but with poor local optima problem. The proposed method is on the basis of PSO as well as Tabu search algorithms. Results show the efficiency of the proposed Tabu PSO by enhancing the number of clusters formed, percentage of nodes alive and shows the reduction of average packet loss rate and average end to end delay.
Component degradation in power electronic converters severely threatens the system's reliability. These components degrade over time due to switching action, and this phenomenon is further aggravated ...with wide band gap devices. For ensuring system reliability and accurate degraded component identification, the development of a real-time noninvasive health monitoring mechanism is desired. This article develops and validates a real-time digital twin (DT)-based condition monitoring for multiphase interleaved boost converters. The DT model is based on an actual state-space modeling approach which is solved numerically using Runge-Kutta fourth to mimic the physical system. Then, the output signals from physical hardware and the DT model are compared to find the least squared error-based multiobjective optimization problem. A metaheuristic approach like particle swarm optimization and genetic algorithm is used to estimate the health of components of the converter. The proposed methodology is extendable to different inductor coupling strategies under continuous-conduction-mode and discontinuous-conduction-mode operations. The idea is to generalize the DT modeling concept for condition monitoring. Moreover, the article proposes decoupling and hybrid approaches to improve estimation accuracy by 9.4% and reduce embedded computational requirements by 22%, respectively. A 75 kW, 60-kHz SiC IBC hardware prototype is built and tested for concept validation. Notably, the challenges and impact of various sensing integrity errors encountered during condition monitoring are also discussed. Finally, the article discusses novel pre and postprocessing steps for improving estimation accuracy and robustness in the case of control, sensing, and operating condition variability.
Recently, visible light communication (VLC) has gradually become a research hotspot in indoor environments because its advantages of illumination and relative high positioning accuracy. But ...unfortunately, in the matter of algorithm complexity and positioning accuracy, most existing VLC-based systems fail to deliver satisfactory performance. Moreover, the majority of visible light positioning algorithm in them are based on two-dimensional (2-D) plane. In addition, some of the systems realize 3-D positioning on the base of various sensors or hybrid complex algorithm. These methods greatly reduce the robustness of VLC system. To solve these problems, a novel VLC positioning system based on modified particle swarm optimization (PSO) algorithm is put forward in this article. PSO is a powerful population-based stochastic approach to solve the global optimization problems, such as VLC-based indoor positioning, which can be transformed into a global optimization problem. Our simulation shows that the average distance error is 3.9 mm within 20 iterations in an indoor environment of 3m × 3m × 4m. And the positioning results prove that this system can prove high localization accuracy and significantly lower the algorithm complexity. Moreover, in the experiment, we come up with a solution that using Kalman filter to deal with the unstable received signals. Our experiment result proves the mentioned system satisfies the requirement of cm-level indoor positioning. Therefore, this scheme may be considered as one of the competitive indoor positioning candidates in the future.
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