Immune Particle Swarm Optimization Research Articles

The Research on the Distribution Network Reconfiguration and the Application of Immune Particle Swarm Optimization

The Research on the Distribution Network Reconfiguration and the Application of Immune Particle Swarm Optimization

Modeling and identification of the multi-axis drive system using support vector machine and granular computing

Advanced manufacturing technology requires high-precision capability in multi-axis computer numerical control (CNC) machine tools. At present, the modeling and identification for the drive system of CNC machine tools has some defects. In order to solve the problem, some interdisciplinary theories and methods, such as support vector machines, granular computing, artificial immune algorithms, and particle swarm optimization algorithms, have been used to model and identify multi-axis drive systems for CNC machine tools. An identification method using a support vector machine, based on granular computing, is presented to identify a multi-axis servo drive system model for improving the precision of model identification, and an immune particle swarm optimization algorithm, based on crossover and mutation functions, is proposed to optimize the structure parameters of the support vector machine based on granular computing. The proposed identification method was evaluated by experiments using the multi-axis servo drive system. The experimental results showed that the proposed approach is capable of improving modeling and identification precision.

GPU-Accelerated Parallel Coevolutionary Algorithm for Parameters Identification and Temperature Monitoring in Permanent Magnet Synchronous Machines

A hierarchical fast parallel co-evolutionary immune particle swarm optimization (PSO) algorithm, accelerated by graphics processing unit (GPU) technique (G-PCIPSO), is proposed for multiparameter identification and temperature monitoring of permanent magnet synchronous machines (PMSM). It is composed of two levels and is developed based on compute unified device architecture (CUDA). In G-PCIPSO, the antibodies (Abs) of higher level memory are selected from the lower level swarms and improved by immune clonal-selection operator. The search information exchanges between swarms using the memory-based sharing mechanism. Moreover, an immune vaccine-enhanced operator is proposed to lead the ${P}$ bests particles to unexplored areas. Optimized parallel implementations of G-PCIPSO algorithm is developed on GPU using CUDA, which significantly speeds up the search process. Finally, the proposed algorithm is applied to multiple parameters identification and temperature monitoring of PMSM. It can track parameter variation and achieve temperature monitoring online effectively. Compared with a CPU-based serial execution, the computational efficiency is greatly enhanced by GPU-accelerated parallel computing technique.

Immune particle swarm optimization of linear frequency modulation in acoustic communication

With the exploration of the ocean, underwater acoustic communication has attracted more and more attention in recent years. The underwater acoustic channel is considered to be one of the most complicated channels because it suffers from more serious multipath effect, fewer available bandwidths and quite complex noise. Since the signals experience a serious distortion after being transmitted through the underwater acoustic channel, the underwater acoustic communication experiences a high bit error rate (BER). To solve this problem, carrier waveform inter-displacement (CWID) modulation is proposed. It has been proved that CWID modulation is an effective method to decrease BER. The linear frequency modulation (LFM) carrier-waves are used in CWID modulation. The performance of the communication using CWID modulation is sensitive to the change of the frequency band of LFM carrier-waves. The immune particle swarm optimization (IPSO) is introduced to search for the optimal frequency band of the LFM carrier-waves, due to its excellent performance in solving complicated optimization problems. The multi-objective and multi-peak optimization nature of the IPSO gives a suitable description of the relationship between the upper band and the lower band of the LFM carrier-waves. Simulations verify the improved performance and effectiveness of the optimization method.

Hybrid customer requirements rating method for customer-oriented product design using QFD

Quality function deployment (QFD) is a well-known customer-oriented product design methodology. Rating the final importance of customer requirements (CRs) is really a very essential starting point in the implementation of QFD, since it largely affects the target setting value of design requirements. This paper aims to propose a novel method to deal with the relative importance ratings (RIRs) of CRs problem considering customers' diversified requirements and unknown information on customers' weights, which is an indispensable process for determining the final importance ratings of CRs. First, a new concept of customer's assessment structure is proposed according to the basic idea of grey relational analysis (GRA), and then a constrained nonlinear optimization model is constructed to describe the assessment information aggregation factors of CRs considering customers' personalized and diversified requirements. Furthermore, an immune particle swarm optimization (IPSO) algorithm is designed to solve the model, and the weight vector of customers is obtained. Finally, a car door design example is introduced to illustrate the novel hybrid GRA-IPSO method's potential application in determining the RIRs of CRs.

Comparison of Artificial Immune System and Particle Swarm Optimization Techniques for Error Optimization of Machine Vision Based Tool Movements

In conventional tool positioning technique, sensors embedded in the motion stages provide the accurate tool position information. In this paper, a machine vision based system and image processing technique for motion measurement of lathe tool from two-dimensional sequential images captured using charge coupled device camera having a resolution of 250 microns has been described. An algorithm was developed to calculate the observed distance travelled by the tool from the captured images. As expected, error was observed in the value of the distance traversed by the tool calculated from these images. Optimization of errors due to machine vision system, calibration, environmental factors, etc. in lathe tool movement was carried out using two soft computing techniques, namely, artificial immune system (AIS) and particle swarm optimization (PSO). The results show better capability of AIS over PSO.

Optimal location of UPFC controller in transmission network using hybrid chemical reaction optimization algorithm

This paper presents a new hybrid chemical reaction optimization (HCRO) approach which is based on chemical reaction optimization (CRO) and differential evolution (DE) to find the optimal placement and parameter setting of unified power flow controller (UPFC) to achieve optimal performance of power system network. In the proposed algorithm, four elementary reactions, i.e., on-wall ineffective collision, inter-molecular ineffective collision, decomposition, and synthesis, are developed. Moreover, mutation operation of DE is integrated with inter-molecular ineffective collision and crossover operation is introduced in the inter-molecular collision, synthesis, and decomposition process to accelerate the convergence speed and improve the solution quality of CRO algorithm. Here, three different single objectives namely, minimization of the overall cost, transmission loss, voltage deviation and one multi-objective which simultaneously minimizes the transmission loss and voltage deviation are used. To verify the effectiveness, the proposed HCRO approach is implemented on IEEE 14-bus and IEEE 30-bus power systems. Moreover, to establish the superiority, the simulation results of the proposed HCRO technique are compared to the CRO and other previously reported algorithms published in the literature such as genetic algorithm (GA), particle swarm optimization (PSO), immune GA (IGA), immune PSO (IPSO) and hybrid immune algorithm (HIA). It is found that the results obtained by the proposed HCRO technique are superior to those obtained by other discussed algorithms.

Application of the K-Means Immune Particle Swarm Optimization Algorithm in the Steam Generator Water Level Control

The Stability of SG water level plays an important role in the safety of nuclear power plants, but tuned the parameter of water level PID controller is hard. Proposed a novel algorithm, KIPSO, which tuning PID controller parameters. Determine the cluster centre through K-means value cluster algorithm, and take the cluster territory as the characteristic value of vaccine set, enhance the vaccine multiplicity. Updated vaccine extraction by self-adaptive method, improved the convergence and adaptability. Analyzed the algorithm robustness in detail, and gave the rule which the immunity selection parameter. The simulation results shows: compares with the PID controller whose parameters are tuned by ZN method, KIPSO have a smaller overshoot, a better stability, and a shorter adjustment time. The simulation results show that the proposed method is effective for tuning PID parameters.

Financial Contagion: A Propagation Simulation Mechanism

Abstract: A simulation mechanism is designed for crisis propagation accommodating contagion. A new co-evolutionary market model is described, where some of the technical traders change their behaviour during crisis and their decisions become largely influenced by market sentiment rather than based on fundamental factors and underlying strategies. Analyzing agents’ behaviour, it is observed that the herd mentality intensifies during crisis. This paper focuses on the transformation of market interdependence into contagion, and on contagion effects. A multi-national platform is build first, to allow different type of players to implement their trading strategies while considering information from both domestic and foreign markets. Traders’ strategies and the performance of the simulated domestic market is trained using historic prices of domestic and foreign markets, while optimizing artificial markets’ parameters through immune particle swarm optimization techniques. Further elements are introduced contributing to the transformation of technical into herd traders. A GARCH-copula is applied next to calculate the tail dependence between the affected market and the origin of the crisis. That parameter is used in the fitness function for selecting the best solutions within the evolving population of possible model parameters, and therefore in the optimization criteria for contagion simulation. The results show that the proportion of herd traders increases in the net market order for optimum contagion simulations. While technical traders’ behaviour corresponds to propagating a crisis through interdependence, herd behaviour corresponds to propagating through contagion. If contagion could be avoided or transformed back to interdependence, with the timely response of national governments and international institutions, a crisis would be more manageable. Further research could introduce a recovery mechanism… into the model through the design of national and international intervention.

A New Algorithm for Demand Prediction of Fresh Agricultural Product Supply Chain

Demand prediction plays a key role in supply chain management of fresh agricultural products enterprises and its algorithm research is a hotspot for the researchers related. A new algorithm for demand prediction of supply chain management of fresh agricultural products is advanced based on BP neural network and immune genetic particle swarm optimization algorithm. First, the deficiencies of traditional BP demand prediction models are analyzed. Second, the BP neural network and immune genetic particle swarm optimization algorithm are integrated and some measures are taken to overcome the deficiencies of traditional BP demand prediction models and calculation flows of the presented algorithm are redesigned. Finally, the presented algorithm is realized with the data from certain fresh agricultural products supply chain and the experimental results verify that the new algorithm can improve effectiveness and validity of demand prediction for fresh agricultural products supply chain.

Reactive Power Optimization Based on Parallel Immune Particle Swarm Optimization

Reactive power optimization is important to ensure power quality, improve system security, and reduce active power loss. So, this paper proposed parallel immune particle swarm optimization (PIPSO) algorithm. This algorithm makes basic particle swarm optimization (BPSO) and discrete particle swarm optimization (DPSO) to optimize in parallel, and improves the convergence capability of particle swarm optimization with convergence ability. It is effective to overcome the problem of local convergence by immune operator, at the same time, it is more reasonable to solve the complex coding problem which discrete variables and continuous variables mixed by parallel optimization. Finally, the simulation results of IEEE-14, IEEE-30, IEEE-118 nodes system show that compared to the genetic algorithm and basic particle swarm optimization, the parallel immune particle swarm optimization can achieve the convergence effect faster and more stable, and better to solve the large-scale power system reactive power optimization.

Harmonic Suppression Method Based on Immune Particle Swarm Optimization Algorithm in Micro-Grid

Distributed generation has attracted great attention in recent years, thanks to the progress in new-generation technologies and advanced power electronics. And micro-grid can make full use of distributed generation, so it has been widespread concern. On the other hand due to the extensive use of power electronic devices and many of the loads within micro-grid are nonlinear in nature, Micro-grid generate a large number of harmonics, so harmonics pollution needs to be addressed. Usually we use passive filter to filter out harmonic, in this paper, we propose a new method to optimize the filter parameters, so passive filter can filter out harmonic better. This method utilizes immune particle swarm optimization algorithm to optimize filter parameters. It can be shown from the simulation results that the proposed method is effective for micro-grid voltage harmonics compensation.

Research on a kind of assembly sequence planning based on immune algorithm and particle swarm optimization algorithm

In order to solve the assembly sequence planning of a certain type of product (e.g., product is composed of different types of parts and each type has several identical parts), first of all, the rule of nomenclature for this type of product is designed. Secondly, geometric feasibility and coherence are designed as constraint conditions, and these two constrains and processability are combined with each other as the objective function. Finally, the proposed novel method under the name of immune particle swarm optimization algorithm is tested. The results show that the proposed immune particle swarm algorithm is an efficient approach to solve the assembly sequence planning problem.

Application of BP Neural Network Based on Immune Particle Swarm Optimization for Fault Diagnosis of Power Transformer

Cloing and hypermutation of immune theory were used in optimization on particle swarm optimization (PSO), an immune particle swarm optimization (IPSO) algorithm was proposed , which overcome the problem of premature convergence on PSO. IPSO was used in BP Neural Network training to overcome slow convergence speed and easily getting into local dinky value of gradient descent algorithm. BP Neural Network trained by IPSO was used to fault diagnosis of power transformer, it has high accuracy after experimental verification and to meet the power transformer diagnosis engineering requirements.

Research on Knowledge Acquisition Based on Improved Particle Swarm Optimization

In order to solve the problem of knowledge acquisition in equipment fault diagnosis,the paper introduces a method based on improved particle swarm optimization. Firstly the paper transforms the nonlinear equations which describe the system into optimization problem with constriction. Since the equation is nonlinear and multidimensional ,standard particle swarm cant solve the problem due to the weakness of premature. So one improved particle swarm optimization is proposed. During the evolution, density evaluation, clone and mutation operator is proposed under the thought of immunity. The results of simulation show that the immune particle swarm optimization can simulate effectively and acquire the system knowledge.

An Unmanned Helicopter Model Identification Method Based on the Immune Particle Swarm Optimization Algorithm

An unmanned helicopter dynamic model identification method based on immune particle swarm optimization (PSO) algorithm is approved in this paper. In order to improve the search efficiency of PSO and avoid the premature convergence, the PSO algorithm is combined with the immune algorithm. The unmanned helicopter model parameters are coded as particle, the error of flight test and math simulation model is objective function, and the dynamic model of unmanned helicopter is identified. The simulation result shows that the method has high identification precision and can realistically reflect the dynamic characteristics.

Parametric Identification of Bouc-Wen Hysteresis Model for Piezoelectric Ceramic Actuator Based on Immune Particle Swarm Optimization Algorithm

This paper is concerned with the parametric identification based on immune particle swarm optimization (IPSO) algorithm for Bouc-Wen model of piezoelectric ceramic actuators (PCA). IPSO algorithm is an algorithm that introduces the immunological memory and selection mechanism of concentration of antibody of immune system to PSO algorithm. It is used to identify unknown parameters of Bouc-Wen model, one of the most widely used parametric models of hysteresis in mechanics. The experimental results reveal that IPSO algorithm has better precision and convergence.

A novel artificial immune clonal selection classification and rule mining with swarm learning model

Metaheuristic optimisation algorithms have become popular choice for solving complex problems. By integrating Artificial Immune clonal selection algorithm (CSA) and particle swarm optimisation (PSO) algorithm, a novel hybrid Clonal Selection Classification and Rule Mining with Swarm Learning Algorithm (CS2) is proposed. The main goal of the approach is to exploit and explore the parallel computation merit of Clonal Selection and the speed and self-organisation merits of Particle Swarm by sharing information between clonal selection population and particle swarm. Hence, we employed the advantages of PSO to improve the mutation mechanism of the artificial immune CSA and to mine classification rules within datasets. Consequently, our proposed algorithm required less training time and memory cells in comparison to other AIS algorithms. In this paper, classification rule mining has been modelled as a miltiobjective optimisation problem with predictive accuracy. The multiobjective approach is intended to allow the PSO algorithm to return an approximation to the accuracy and comprehensibility border, containing solutions that are spread across the border. We compared our proposed algorithm classification accuracy CS2 with five commonly used CSAs, namely: AIRS1, AIRS2, AIRS-Parallel, CLONALG, and CSCA using eight benchmark datasets. We also compared our proposed algorithm classification accuracy CS2 with other five methods, namely: Naïve Bayes, SVM, MLP, CART, and RFB. The results show that the proposed algorithm is comparable to the 10 studied algorithms. As a result, the hybridisation, built of CSA and PSO, can develop respective merit, compensate opponent defect, and make search-optimal effect and speed better.

Solving Unconstrained Global Optimization Problems via Hybrid Swarm Intelligence Approaches

Stochastic global optimization (SGO) algorithms such as the particle swarm optimization (PSO) approach have become popular for solving unconstrained global optimization (UGO) problems. The PSO approach, which belongs to the swarm intelligence domain, does not require gradient information, enabling it to overcome this limitation of traditional nonlinear programming methods. Unfortunately, PSO algorithm implementation and performance depend on several parameters, such as cognitive parameter, social parameter, and constriction coefficient. These parameters are tuned by using trial and error. To reduce the parametrization of a PSO method, this work presents two efficient hybrid SGO approaches, namely, a real-coded genetic algorithm-based PSO (RGA-PSO) method and an artificial immune algorithm-based PSO (AIA-PSO) method. The specific parameters of the internal PSO algorithm are optimized using the external RGA and AIA approaches, and then the internal PSO algorithm is applied to solve UGO problems. The performances of the proposed RGA-PSO and AIA-PSO algorithms are then evaluated using a set of benchmark UGO problems. Numerical results indicate that, besides their ability to converge to a global minimum for each test UGO problem, the proposed RGA-PSO and AIA-PSO algorithms outperform many hybrid SGO algorithms. Thus, the RGA-PSO and AIA-PSO approaches can be considered alternative SGO approaches for solving standard-dimensional UGO problems.

Research of Particle Filter Based on Immune Particle Swarm Optimization

Research of Particle Filter Based on Immune Particle Swarm Optimization