Adaptive Inertia Weight Factor Research Articles

Layout of Detection Array Based on Multi-Strategy Fusion Improved Adaptive Mayfly Algorithm in Bearing-Only Sensor Network.

The various applications of bearing-only sensor networks for detection and localization are becoming increasingly widespread and important. The array layout of the bearing-only sensor network seriously impacts the detection performance. This paper proposes a multi-strategy fusion improved adaptive mayfly algorithm (MIAMA) in a bearing-only sensor network to perform layout planning on the geometric configuration of the optimal detection. Firstly, the system model of a bearing-only sensor network was constructed, and the observability of the system was analyzed based on the Cramer-Rao Lower Bound and Fisher Information Matrix. Then, in view of the limitations of the traditional mayfly algorithm, which has a single initial population and no adaptability and poor global search capabilities, multi-strategy fusion improvements were carried out by introducing Tent chaos mapping, the adaptive inertia weight factor, and Random Opposition-based Learning. Finally, three simulation experiments were conducted. Through comparison with the Particle Swarm Optimization (PSO) algorithm, Mayfly Algorithm (MA), and Genetic Algorithm (GA), the effectiveness and superiority of the proposed MIAMA were validated.

Trajectory analysis and optimization of sea buckthorn fruit vibration separation manipulator based on I-PSO algorithm

In this paper, the optimal time planning of vibration separation trajectory of Hippophae rhamnoides fruit is studied for space manipulator using the I-PSO algorithm. The first step is to analyze the motion of the robotic arm's joints, which are limited in range and speed, in combination with a 3–5–3 polynomial interpolation, an improved Particle swarm optimization with adaptive inertia weight and asynchronous learning factor is proposed, and the specific process is given. Experimental images and data show that the improved particle swarm optimization algorithm can ensure the continuity of joint acceleration and velocity, and the optimal vibration trajectory time is 0.536539094 s Compared with the planned system trajectory time of 0.71022 s, the speed increased by 24.5%. The results of the orthogonal experiment show that the average fruit drop rate reaches 96.19%, which verifies the validity and reliability of the I-PSO algorithm for optimal time planning of seabuckthorn fruit separation vibration trajectory.

Research on Optimal Configuration of Distributed Generation based on Multi-scenario Analysis

In order to make the grid-connected planning of distributed generation more reasonable, the uncertainties of intermittent distributed generation output and load forecasting are included in the solution process. Firstly, multi-scenario analysis is introduced to transform the source load uncertainty problem into deterministic problem, and the Latin super-force sampling method is used to generate the initial planning scene. The density peak clustering idea and elbow method are used to improve the K-means clustering algorithm and reduce the scene. Secondly, the optimal allocation model of grid-connected distributed generation is constructed with the minimum annual comprehensive cost as the objective function. Finally, in view of the slow convergence speed and easy to fall into local optimum of particle swarm optimization (PSO), an adaptive inertia weight factor is adopted to improve PSO, and the effectiveness of the proposed model and method is verified by IEEE 33-node standard simulation example.

Research on Improved PSO Algorithm for Multi-peak to Photovoltaic Maximum Power Tracking Control

To ensure the real-time output maximum power of a photovoltaic power producing system and improve the multi-peak MPPT management of a solar array under partial shadow, a maximum power tracking control strategy based on an Improved PSO algorithm is presented. To increase the stability of maximum power tracking and to promote the coordination performance of local development and global search, the adaptive inertia weight and learning factor are developed regarding the basic PSO algorithm. Besides, simulation studies are used to evaluate the MPPT control effects of the method with the fundamental PSO algorithm in various situations. The findings show that the improved PSO algorithm outperforms the traditional PSO technique in terms of tracking effectiveness. It can not only converge to the GMPP quickly in the case of various local shading and light mutations but also has higher power generation efficiency.

Multi-Node Path Planning of Electric Tractor Based on Improved Whale Optimization Algorithm and Ant Colony Algorithm

Under the “Double Carbon” background, the development of green agricultural machinery is very fast. An important factor that determines the performance of electric farm machinery is the endurance capacity, which is directly related to the running path of farm machinery. The optimized driving path can reduce the operating loss and extend the mileage of agricultural machinery, then multi-node path planning helps to improve the working efficiency of electric tractors. Ant Colony Optimization (ACO) is often used to solve multi-node path planning problems. However, ACO has some problems, such as poor global search ability, few initial pheromones, poor convergence, and weak optimization ability, which is not conducive to obtaining the optimal path. This paper proposes a multi-node path planning algorithm based on Improved Whale Optimized ACO, named IWOA-ACO. The algorithm first introduces reverse learning strategy, nonlinear convergence factor, and adaptive inertia weight factor to improve the global and local convergence ability. Then, an appropriate evaluation function is designed to evaluate the solving process and obtain the best fitting parameters of ACO. Finally, the optimal objective function, fast convergence, and stable operation requirements are achieved through the best fitting parameters to obtain the global path optimization. The simulation results show that in flat environment, the length and energy consumption of IWOA-ACO planned path are the same as those of PSO-ACO, and are 0.61% less than those of WOA-ACO. In addition, in bump environment, the length and energy consumption of IWOA-ACO planned path are 1.91% and 4.32% less than those of PSO-ACO, and are 1.95% and 1.25% less than those of WOA-ACO. Therefore, it is helpful to improve the operating efficiency along with the endurance of electric tractors, which has practical application value.

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.

Optimization dispatching of isolated island microgrid based on improved particle swarm optimization algorithm

Aiming at the microgrid system including wind turbine, microgas turbine, diesel generator, fuel cell and battery under the isolated island mode, the optimization dispatching model was established by taking the comprehensive cost considering economy and environmental protection as the objective function and combining with the constraints of system power balance and output limit of each unit. In view of the prematurity and convergence problems of the standard particle swarm optimization algorithm, an improved PSO algorithm with adaptive inertia weight and contraction factor is proposed to enhance the global and local search capability of the algorithm. Finally, the simulation analysis is carried out based on the operation data of typical daily island microgrid. The results show that the improved PSO algorithm adopted in this paper effectively reduces the comprehensive target cost and achieves good optimization effect.

Fault Detection of Wind Turbine Gearboxes Based on IBOA-ERF.

As one of the key components of wind turbines, gearboxes are under complex alternating loads for a long time, and the safety and reliability of the whole machine are often affected by the failure of internal gears and bearings. Aiming at the difficulty of optimizing the parameters of wind turbine gearbox fault detection models based on extreme random forest, a fault detection model with extreme random forest optimized by the improved butterfly optimization algorithm (IBOA-ERF) is proposed. The algebraic sum of the false alarm rate and the missing alarm rate of the fault detection model is constructed as the fitness function, and the initial position and position update strategy of the individual are improved. A chaotic mapping strategy is introduced to replace the original population initialization method to enhance the randomness of the initial population distribution. An adaptive inertia weight factor is proposed, combined with the landmark operator of the pigeon swarm optimization algorithm to update the population position iteration equation to speed up the convergence speed and improve the diversity and robustness of the butterfly optimization algorithm. The dynamic switching method of local and global search stages is adopted to achieve dynamic balance between global exploration and local search, and to avoid falling into local optima. The ERF fault detection model is trained, and the improved butterfly optimization algorithm is used to obtain optimal parameters to achieve fast response of the proposed model with good robustness and generalization under high-dimensional data. The experimental results show that, compared with other optimization algorithms, the proposed fault detection method of wind turbine gearboxes has a lower false alarm rate and missing alarm rate.

Research on Function Optimization Based on Improved Genetic Particle Swarm Optimization

Aiming at the shortcomings of single algorithm such as blind operation, directionless, long calculation time, low accuracy in Genetic Algorithm and poor diversity of population of Particle Swarm Optimization, which is prone to premature and fall into local optimum. In this study, the serial algorithm fusion idea is adopted, and the population evolved from the Genetic Algorithm is taken as the initial population to be executed by the Particle Swarm Algorithm, namely GAPSO algorithm. In view of the drawbacks of the respective algorithms in the execution of GAPSO algorithm, this paper proposes an Improved Genetic Particle Swarm Optimization algorithm, namely IGAPSO algorithm. In this algorithm, the improved GA only optimizes the initial particle once and then gives it to the improved PSO for optimization. Its optimization method and ability are greatly improved, resulting in a rapid increase in convergence speed. In addition, the improved PSO introduces adaptive inertia weight and learning factor, so that the particles can adaptively adjust global search and local search, and adaptively balance the influence of self-experience and social experience, which greatly avoids falling into local optimization problem and makes the solution more accurate. Through the verification of test functions, GAPSO has obviously improved the calculation accuracy, convergence speed and global stability compared with a single algorithm, while IGAPSO has also improved the convergence accuracy and speed compared with GAPSO, especially in multi-peak functions.

An Optimization Method for Intrusion Detection Classification Model Based on Deep Belief Network

The rapid development and popularization of the network have brought many problems to network security. Intrusion detection technology is often used as an effective security technology to protect the network. The deep belief network (DBN), as a classic model of deep learning, has good classification performance and is often used in the field of intrusion detection. However, the network structure of DBN is generally set through practical experience. For the optimization problem of the DBN-based intrusion detection classification model (DBN-IDS), this paper proposes a new joint optimization algorithm to optimize the DBN's network structure. First, we design a particle swarm optimization (PSO) based on the adaptive inertia weight and learning factor. Second, we use the fish swarm behavior of cluster, foraging, and other behaviors to optimize the PSO to find the initial optimization solution. Then, based on the initial optimization solution, we use the genetic operators with self-adjusting crossover probability and mutation probability to optimize the PSO to search the global optimization solution. Finally, the global optimization solution constructed by the above-mentioned joint optimization algorithm is used as the network structure of the intrusion detection classification model. The experimental results show that compared with other DBN-IDS optimization algorithms, our algorithm shortens the average detection time by at least 24.69% on the premise of increasing the average training time by 6.9%; compared with the tested classification algorithms, our DBN-IDS improves the average classification accuracy by at least 1.3% and up to 14.80% in the five-category classification, which is proved to be an efficient DBN-IDS optimization method.

DYNAMIC ECONOMIC DISPATCH USING MODIFIED BACTERIAL FORAGING ALGORITHM ORIENTED WITH WEIGHTED PARTICLE SWARM OPTIMIZATION

The goal of dynamic economic dispatch (DED) is to minimize the total operating cost of all committed generating units considering all physical and operational constraints. The DED problem is very complicated nonlinear optimization problem due to transmission losses, ramp-rate limits and valve-point effects constraints. Both bacterial foraging algorithm (BFA) and particle swarm optimization (PSO) methods have poor convergence characteristics. So, weighted PSO (WPSO) is employed to avoid the drawbacks of original PSO by employing the adaptive inertia weight factor. In this paper, modified BFA (MBFA) oriented with WPSO method is proposed by integrating MBFA and WPSO. By combining these two algorithms, the advantages of both of them can be extracted. This leads to get better solution of DED problem. Also, the diversity strategy is used in the proposed algorithm to solve the problem of early convergence. To prove the efficiency of the proposed method in solving the DED problem, different systems are employed. The obtained results prove the efficiency of the proposed method when comparing with other state of the art methods.

Adaptive PSO for optimal LQR tracking control of 2 DoF laboratory helicopter

This paper deals with the attitude tracking control problem for a 2 DoF laboratory helicopter using optimal linear quadratic regulator (LQR). As the performance of the LQR controller greatly depends on the weighting matrices (Q and R), it is important to select them optimally. However, normally the weighting matrices are selected based on trial and error approach, which not only makes the controller design tedious but also time consuming. Hence, to address the weighting matrices selection problem of LQR, in this paper we propose an adaptive particle swarm optimization (APSO) method to obtain the elements of Q and R matrices. Moreover, to enhance the convergence speed and precision of the conventional PSO, an adaptive inertia weight factor (AIWF) is introduced in the velocity update equation of PSO. One of the key features of the AIWF is that unlike the standard PSO in which the inertia weight is kept constant throughout the optimization process, the weights are varied adaptively according to the success rate of the particles towards the optimum value. The proposed APSO based LQR control strategy is applied for pitch and yaw axes control of 2 Degrees of Freedom (DoF) laboratory helicopter workstation, which is a highly nonlinear and unstable system. Experimental results substantiate that the weights optimized using APSO, compared to PSO, result in not only reduced tracking error but also improved tracking response with reduced oscillations.

Research on Associated Logistics Transportation Scheduling With Hard Time Windows Based On Chaos PSO

This paper studies the associated logistics transportation scheduling problem with hard time windows and constructs the mathematical model. Optimize the chaos PSO problem using the improved adaptive inertia weight factor method to improve the accuracy and efficiency of the algorithm. Comparing our method with the GA and standard PSO algorithm, our algorithm has better performance with solving such problem.

A Chaotic Particle Swarm Optimization-Based Heuristic for Market-Oriented Task-Level Scheduling in Cloud Workflow Systems.

Cloud workflow system is a kind of platform service based on cloud computing. It facilitates the automation of workflow applications. Between cloud workflow system and its counterparts, market-oriented business model is one of the most prominent factors. The optimization of task-level scheduling in cloud workflow system is a hot topic. As the scheduling is a NP problem, Ant Colony Optimization (ACO) and Particle Swarm Optimization (PSO) have been proposed to optimize the cost. However, they have the characteristic of premature convergence in optimization process and therefore cannot effectively reduce the cost. To solve these problems, Chaotic Particle Swarm Optimization (CPSO) algorithm with chaotic sequence and adaptive inertia weight factor is applied to present the task-level scheduling. Chaotic sequence with high randomness improves the diversity of solutions, and its regularity assures a good global convergence. Adaptive inertia weight factor depends on the estimate value of cost. It makes the scheduling avoid premature convergence by properly balancing between global and local exploration. The experimental simulation shows that the cost obtained by our scheduling is always lower than the other two representative counterparts.

Application of soft computing techniques for multi source deregulated power system

In this paper, an interconnected power system is proposed for Automatic Generation Control (AGC) in restructured power environment. The customized AGC scheme is projected in deregulated environment for multi-source combination of hydro, reheat thermal and gas generating units in entire area. Proportional integral derivative controller is offered for AGC scheme and the gains are optimised through soft computing techniques such as Hybrid Chaotic Particle Swarm Optimization (HCPSO) algorithm, Real Coded Genetic Algorithm (RCGA) and also with Artificial Neural Network (ANN). The PSO chosen here carves out the AGC problem through the addition of adaptive inertia weight factor and adaptive constriction factors. The intense trend in deregulated system leads to the aggressiveness in frequency and tie line power deviations. It is observed that the chaos mapping of PSO enhance the rate of convergence using logistics map sequence. The proposed algorithms are tested on three area power system for different electricity contracted scenarios under various operating conditions with Generation Rate Constraint (GRC). Analysis reveals that proposed HCPSO improves significantly the dynamical performances of system such as settling time and overshoot. The comparative results show the robust performance of HCPSO against parametric uncertainties for a wide range of load demands and disturbances. Key words: Automatic generation control (AGC), hybrid chaotic particle swarm optimisation (HCPSO), proportional integral derivative (PID), restructured power system.

Analysis of the Reactive Power Control Strategy Based on Improved Particle Swarm Algorithm

According to the number of constraints of the reactive power control equipment, this paper considers the constraints of the voltage in the load of extreme points of the segmentation program period, and uses dynamic optimization of sub-segmentation in the load curve. It solves the optimization based on applying improved particle swarm optimization into load curve segments, and uses particle swarm optimization through adaptive inertia weight and acceleration factor, phase initialization and mutation operations in order to improve the ability of global optimization.

Nonconvex resource control and lifetime optimization in wireless video sensor networks based on chaotic particle swarm optimization

Wireless video sensor networks (WVSNs) have attracted a lot of interest because of the enhancements that they offer to existing wireless sensor networks applications and their numerous potential in other research areas. However, the introduction of video raises new challenges. The transmission of video and imaging data requires both energy efficiency and quality of service (QoS) assurance in order to ensure the efficient use of sensor resources as well as the integrity of the collected information. To this end, this paper proposes a joint power, rate and lifetime management algorithm in WVSNs based on the network utility maximization framework. The optimization problem is always nonconcave, which makes the problem difficult to solve. This paper makes progress in solving this type of optimization problems using particle swarm optimization (PSO). Based on the movement and intelligence of swarms, PSO is a new evolution algorithm to look for the most fertile feeding location. It can solve discontinuous, nonconvex and nonlinear problems efficiently. First, since chaotic mapping enjoys certainty, ergodicity and the stochastic property, the paper introduces chaos mapping into PSO with adaptive inertia weight factor to avoid the disadvantage of original PSO of easily getting to the local optimal solution in the later evolution period and keep the rapid convergence performance. Second, based on the distribution characteristics of the actual network, we decompose the resource control problem into a number of sub-problems using the hierarchical thought, where each user corresponds to a subsystem which is solved using the proposed CPSO3 method. Through the cooperative coevolution theory, these sub-optimization problems interact with each other to obtain the optimum of the system. Numerical examples show that our algorithm can guarantee fast convergence and fairness within a few iterations. Besides, it is demonstrated that our algorithm can solve the nonconvex optimization problems very efficiently.

Study of Adaptive Chaos Embedded Particle Swarm Optimization Algorithm

Chaos particle swarm optimization (CPSO) can not guarantee the population multiplicity and the optimized ergodicity, because its algorithm parameters are still random numbers in form. This paper proposes a new adaptive chaos embedded particle swarm optimization (ACEPSO) algorithm that uses chaotic maps to substitute random numbers of the classical PSO algorithm so as to make use of the properties of stochastic and ergodicity in chaotic search and introduces an adaptive inertia weight factor for each particle to adjust its inertia weight factor adaptively in response to its fitness, which can overcome the drawbacks of CPSO algorithm that is easily trapped in local optima. The experiments with complex and Multi-dimensional functions demonstrate that ACEPSO outperforms the original CPSO in the global searching ability and convergence rate.

A novel chaotic particle swarm optimization based fuzzy clustering algorithm

Clustering is a popular data analysis and data mining technique. In this paper, a novel chaotic particle swarm fuzzy clustering (CPSFC) algorithm based on chaotic particle swarm (CPSO) and gradient method is proposed. Fuzzy clustering model optimization is challenging, in order to solve this problem, adaptive inertia weight factor (AIWF) and iterative chaotic map with infinite collapses (ICMIC) are introduced, and a new CPSO algorithm combined AIWF and ICMIC based chaotic local search is studied. The CPSFC algorithm utilizes CPSO to search the fuzzy clustering model, exploiting the searching capability of fuzzy c-means (FCM) and avoiding its major limitation of getting stuck at locally optimal values. Meanwhile, gradient operator is adopted to accelerate convergence of the proposed algorithm. Its superiority over the FCM algorithm and another two global optimization algorithm-based clustering methods is extensively demonstrated for several artificial and real life data sets in comparative experiments.

Chaotic particle swarm optimization based robust load flow

A reliable load flow algorithm based on chaotic particle swarm optimization (CPSO) technique has been developed. To obtain optimum solution efficiently and accurately, an innovative formula for adaptive inertia weight factor (AIWF) has been introduced. Novel formulae for constriction factors have been designed for the load flow problems which are also adaptive. In addition to that, chaotic local search (CLS) is used with PSO to get rid of the local optima. To the best of our knowledge, it is the first report of applying CPSO to solve load flow problems. The efficiency and effectiveness of the proposed algorithm has been tested on different standard and ill-conditioned test systems. The proposed method shows its robustness under critical conditions when conventional load flow methods fail.