Multi-objective reservoir operation using particle swarm optimization with adaptive random inertia weights

Based on conventional particle swarm optimization(PSO),this paper presents an efficient and reliable heuristic approach using PSO with an adaptive random inertia weight(ARIW)strategy,referred to as the ARIW-PSO algorithm,to build a multi-objective optimization model for reservoir operation.Using the triangular probability density function,the inertia weight is randomly generated,and the probability density function is automatically adjusted to make the inertia weight generally greater in the initial stage of evolution,which is suitable for global searches.In the evolution process,the inertia weight gradually decreases,which is beneficial to local searches.The performance of the ARIWPSO algorithm was investigated with some classical test functions,and the results were compared with those of the genetic algorithm(GA),the conventional PSO,and other improved PSO methods.Then,the ARIW-PSO algorithm was applied to multi-objective optimal dispatch of the Panjiakou Reservoir and multi-objective flood control operation of a reservoir group on the Luanhe River in China,including the Panjiakou Reservoir,Daheiting Reservoir,and Taolinkou Reservoir.The validity of the multi-objective optimization model for multi-reservoir systems based on the ARIW-PSO algorithm was verified.

Creating Smart House via IoT and Intelligent Computation

This study mainly uses the concept of the Internet of Things(IoT)to establish a smart house with an indoor,comfortable,environmental,and real-time monitoring system.In the smart house,this investigation employed the temperature-and humidity-sensing module and the lightness module to monitor any con-dition for an intelligent-living house.The data of temperature,humidity,and lightness are transmitted wirelessly to the human-machine interface.The correlation of the weight of the extension theory is used to analyze the ideal and comfortable environment so that people in the indoor environment can feel better thermal comfort and lightness.In this study,improved particle swarm optimization(IPSO)is employed—an effective evolutionary method used to search the function extreme.It is simple and has a fast convergence.The convergence accuracy of this algorithm is not high at the beginning,and it can easily fall into the local extreme points.The effect of the inertia weight in mix extension theory and PSO becomes IPSO-Extension Neural Network(ENN),which was analyzed and found reliable.Motivated by the idea of power function,a new non-linear strategy for decreasing inertia weight(DIW)was proposed based on the existing linear DIW.Then,a novel hierarchical multi-sensor data fusion algorithm adopting this strategy was presented,and the weight factor of the data fusion was estimated.The distinctive feature of this algorithm is its capability of fusing data in a near-optimal manner when there is no available information about the reliability of the information sources,the degree of redundancy/complementarities of the information sources,and the structure of the hierarchy.It obtained effective information from the fusion data,successfully removed the noise disturbance,and achieved favorable results.

WACPN:A Neural Network for Pneumonia Diagnosis

Community-acquired pneumonia(CAP)is considered a sort of pneumonia developed outside hospitals and clinics.To diagnose community-acquired pneumonia(CAP)more efficiently,we proposed a novel neural network model.We introduce the 2-dimensional wavelet entropy(2d-WE)layer and an adaptive chaotic particle swarm optimization(ACP)algorithm to train the feed-forward neural network.The ACP uses adaptive inertia weight factor(AIWF)and Rossler attractor(RA)to improve the performance of standard particle swarm optimization.The final combined model is named WE-layer ACP-based network(WACPN),which attains a sensitivity of 91.87±1.37%,a specificity of 90.70±1.19%,a precision of 91.01±1.12%,an accuracy of 91.29±1.09%,F1 score of 91.43±1.09%,an MCC of 82.59±2.19%,and an FMI of 91.44±1.09%.The AUC of this WACPN model is 0.9577.We find that the maximum deposition level chosen as four can obtain the best result.Experiments demonstrate the effectiveness of both AIWF and RA.Finally,this proposed WACPN is efficient in diagnosing CAP and superior to six state-of-the-art models.Our model will be distributed to the cloud computing environment.

Cloud-Verhulst hybrid prediction model for dam deformation under uncertain conditions

Uncertainties existing in the process of dam deformation negatively influence deformation prediction. However, existing deformation pre- diction models seldom consider uncertainties. In this study, a cloud-Verhulst hybrid prediction model was established by combing a cloud model with the Verhulst model. The expectation, one of the cloud characteristic parameters, was obtained using the Verhulst model, and the other two cloud characteristic parameters, entropy and hyper-entropy, were calculated by introducing inertia weight. The hybrid prediction model was used to predict the dam deformation in a hydroelectric project. Comparison of the prediction results of the hybrid prediction model with those of a traditional statistical model and the monitoring values shows that the proposed model has higher prediction accuracy than the traditional sta- tistical model. It provides a new approach to predicting dam deformation under uncertain conditions.

Study of a New Improved PSO-BP Neural Network Algorithm

In order to overcome shortcomings of traditional BP neural network,such as low study efficiency, slow convergence speed,easily trapped into local optimal solution,we proposed an improved BP neural network model based on adaptive particle swarm optimization( PSO) algorithm. This algorithm adjusted the inertia weight coefficients and learning factors adaptively and therefore could be used to optimize the weights in the BP network. After establishing the improved PSO-BP( IPSO-BP) model,it was applied to solve fault diagnosis of rolling bearing. Wavelet denoising was selected to reduce the noise of the original vibration signals,and based on these vibration signals a wide set of features were used as the inputs in the neural network models. We demonstrate the effectiveness of the proposed approach by comparing with the traditional BP,PSO-BP and linear PSO-BP( LPSO-BP) algorithms. The experimental results show that IPSO-BP network outperforms other algorithms with faster convergence speed,lower errors,higher diagnostic accuracy and learning ability.

Improved Whale Optimization Algorithm Based on Mirror Selection

Since traditional whale optimization algorithms have slow convergence speed,low accuracy and are easy to fall into local optimal solutions,an improved whale optimization algorithm based on mirror selection(WOA-MS)is proposed. Specific improvements includes:(1)An adaptive nonlinear inertia weight based on Branin function was introduced to balance global search and local mining.(2) A mirror selection method is proposed to improve the individual quality and speed up the convergence. By optimizing several test functions and comparing the experimental results with other three algorithms,this study verifies that WOA-MS has an excellent optimization performance.

Improved particle swarm optimization algorithm for multi-reservoir system operation

In this paper, a hybrid improved particle swarm optimization （IPSO） algorithm is proposed for the optimization of hydroelectric power scheduling in multi-reservoir systems. The conventional particle swarm optimization （PSO） algorithm is improved in two ways： （1） The linearly decreasing inertia weight coefficient （LDIWC） is replaced by a self-adaptive exponential inertia weight coefficient （SEIWC）, which could make the PSO algorithm more balanceable and more effective in both global and local searches. （2） The crossover and mutation idea inspired by the genetic algorithm （GA） is imported into the particle updating method to enhance the diversity of populations. The potential ability of IPSO in nonlinear numerical function optimization was first tested with three classical benchmark functions. Then, a long-term multi-reservoir system operation model based on IPSO was designed and a case study was carried out in the Minjiang Basin in China, where there is a power system consisting of 26 hydroelectric power plants. The scheduling results of the IPSO algorithm were found to outperform PSO and to be comparable with the results of the dynamic programming successive approximation （DPSA） algorithm.

Optimal Power Flow Solution Using Particle Swarm Optimization Technique with Global-Local Best Parameters

This paper proposes an efficient method for optimal power flow solution （OPF） using particle swarm optimization （PSO） technique. The objective of the proposed method is to find the steady state operation point in a power system which minimizes the fuel cost, while maintaining an acceptable system performance in terms of limits on generator power, line flow limits and voltage limits. In order to improvise the performance of the conventional PSO （cPSO）, the fine tuning parameters- the inertia weight and acceleration coefficients are formulated in terms of global-local best values of the objective function. These global-local best inertia weight （GLBestlW） and global-local best acceleration coefficient （GLBestAC） are incorporated into PSO in order to compute the optimal power flow solution. The proposed method has been tested on the standard IEEE 30 bus test system to prove its efficacy. The results are compared with those obtained through cPSO. It is observed that the proposed algorithm is computationally faster, in terms of the number of load flows executed and provides better results than the conventional heuristic techniques.

A New Class of Hybrid Particle Swarm Optimization Algorithm

A new class of hybrid particle swarm optimization （PSO） algorithm is developed for solving the premature convergence caused by some particles in standard PSO fall into stagnation. In this algorithm, the linearly decreasing inertia weight technique （LDIW） and the mutative scale chaos optimization algorithm （MSCOA） are combined with standard PSO, which are used to balance the global and local exploration abilities and enhance the local searching abilities, respectively. In order to evaluate the performance of the new method, three benchmark functions are used. The simulation results confirm the proposed algorithm can greatly enhance the searching ability and effectively improve the premature convergence.

Enhancement in Channel Equalization Using Particle Swarm Optimization Techniques

This work proposes an improved inertia weight update method and position update method in Particle Swarm Optimization (PSO) to enhance the convergence and mean square error of channel equalizer. The search abilities of PSO are managed by the key parameter Inertia Weight (IW). A higher value leads to global search whereas a smaller value shifts the search to local which makes convergence faster. Different approaches are reported in literature to improve PSO by modifying inertia weight. This work investigates the performance of the existing PSO variants related to time varying inertia weight methods and proposes new strategies to improve the convergence and mean square error of channel equalizer. Also the position update method in PSO is modified to achieve better convergence in channel equalization. The simulation presents the enhanced performance of the proposed techniques in transversal and decision feedback models. The simulation results also analyze the superiority in linear and nonlinear channel conditions.

An Improved Golden Jackal Optimization Algorithm Based on Multi-strategy Mixing for Solving Engineering Optimization Problems

Nowadays,optimization techniques are required in various engineering domains to find optimal solutions for complex problems.As a result,there is a growing tendency among scientists to enhance existing nature-inspired algorithms using various evolutionary strategies and to develop new nature-inspired optimization methods that can properly explore the feature space.The recently designed nature-inspired meta-heuristic,named the Golden Jackal Optimization(GJO),was inspired by the collaborative hunting actions of the golden jackal in nature to solve various challenging problems.However,like other approaches,the GJO has the limitations of poor exploitation ability,the ease of getting stuck in a local optimal region,and an improper balancing of exploration and exploitation.To overcome these limitations,this paper proposes an improved GJO algorithm based on multi-strategy mixing(LGJO).First,using a chaotic mapping strategy to initialize the population instead of using random parameters,this algorithm can generate initial solutions with good diversity in the search space.Second,a dynamic inertia weight based on cosine variation is proposed to make the search process more realistic and effectively balance the algorithm's global and local search capabilities.Finally,a position update strategy based on Gaussian mutation was introduced,fully utilizing the guidance role of the optimal individual to improve population diversity,effectively exploring unknown regions,and avoiding the algorithm falling into local optima.To evaluate the proposed algorithm,23 mathematical benchmark functions,CEC-2019 and CEC2021 tests are employed.The results are compared to high-quality,well-known optimization methods.The results of the proposed method are compared from different points of view,including the quality of the results,convergence behavior,and robustness.The superiority and high-quality performance of the proposed method are demonstrated by comparing the results.Furthermore,to demonstrate its applicability,it is employed to solve four constrained industrial applications.The outcomes of the experiment reveal that the proposed algorithm can solve challenging,constrained problems and is very competitive compared with other optimization algorithms.This article provides a new approach to solving real-world optimization problems.

Optimizing channel cross section in irrigation area using improved cat swarm optimization algorithm

This research aimed to design the channel cross section with low water loss in irrigation areas.The traditional methods and models are based on explicit equations which neglect seepage and evaporation losses with low accuracy.To rectify this problem,in this research,an improved cat swarm optimization(ICSO)was obtained by adding exponential inertia weight coefficient and mutation to enhance the efficiency of conventional cat swarm optimization(CSO).Finally,the Fifth main channel of Jiangdong Irrigation area in Heilongjiang Province was taken as a study area to test the ability of ICSO.Comparing to the original design,the reduction of water loss was 20%with low flow errors.Furthermore,the ICSO was compared with genetic algorithm(GA),the particle swarm optimization(PSO)and cat swarm algorithm(CSO)to verify the effectiveness in the channel section optimization.The results are satisfactory and the method can be used for reliable design of artificial open channels.

Research of service selection algorithm for net-centric simulation task community

Being a new-generation C4ISR system simulation method,the construction approach of net-centric simulation(NCS)is developing toward net-centric from the traditional approach of platform-centric.NCS is mainly completed by the construction of the simulation task community(STC),the key to which being the dynamic integration of the various services spread in the network in order to form a new STC that meets the requirements of different users.In this study,a simulation task community service selection algorithm(STCSSA)is proposed.The main idea of this algorithm is to transform the construction of STC to the searching of optimal multi-objectives services with QoS global constraints.This paper first introduces the QoS model of STC and evaluates the service composition process,then presents the detailed operating process of STCSSA and design of the dynamic inertia weight strategy of the algorithm,and also proposes an optional variation method.Comparative tests were performed on STCSSA with other particle swarm optimization algorithms.It was validated from the perspective of performance that the proposed algorithm has advantages in improving the rate of convergence and avoiding local optimum,and from the perspective of practical application STCSSA also demonstrated feasibility in the construction of large-scale NCS task community.