Inertia Weight Particle Swarm Optimization Research Articles

An Effective Approach for the Synthesis of Unequally Spaced Antenna Array by Estimating Optimum Elements Density on the Aperture

In this letter, a simple, effective, and computationally efficient approach for the synthesis of uniformly excited unequally spaced linear antenna (USLA) arrays is proposed. The proposed approach deals with determination of optimum elements' density on the aperture, which empowers the synthesis technique with flexibility and increased search competency in attaining maximum possible reduction in peak sidelobe level (PSLL). In the present study, an inertia-weight particle swarm optimization (IW-PSO) algorithm is employed for the synthesis of USLA arrays. Numerical analysis on 16- and 32-element linear arrays is carried out to evidence the effectiveness of the proposed approach. The numerical results obtained through our technique are compared to those employing other techniques available in the literature, which reveals that the proposed scheme outperforms other schemes and offers great flexibility as well as enhances the global search ability of the IW-PSO algorithm in achieving the lowest possible PSLL at reduced computational effort.

Computing Adaptive Feature Weights with PSO to Improve Android Malware Detection

Android malware detection is a complex and crucial issue. In this paper, we propose a malware detection model using a support vector machine (SVM) method based on feature weights that are computed by information gain (IG) and particle swarm optimization (PSO) algorithms. The IG weights are evaluated based on the relevance between features and class labels, and the PSO weights are adaptively calculated to result in the best fitness (the performance of the SVM classification model). Moreover, to overcome the defects of basic PSO, we propose a new adaptive inertia weight method called fitness-based and chaotic adaptive inertia weight-PSO (FCAIW-PSO) that improves on basic PSO and is based on the fitness and a chaotic term. The goal is to assign suitable weights to the features to ensure the best Android malware detection performance. The results of experiments indicate that the IG weights and PSO weights both improve the performance of SVM and that the performance of the PSO weights is better than that of the IG weights.

Orthogonal Taguchi-based cat algorithm for solving task scheduling problem in cloud computing

In cloud computing datacenter, task execution delay is a common phenomenal cause by task imbalance across virtual machines (VMs). In recent times, a number of artificial intelligence scheduling techniques are applied to reduced task execution delay. These techniques have contributed toward the need for an ideal solution. The objective of this study is to optimize task scheduling based on proposed orthogonal Taguchi-based cat swarm optimization (OTB-CSO) in order to reduce total task execution delay. In our proposed algorithm, Taguchi orthogonal approach was incorporated into tracing mode of CSO to scheduled tasks on VMs with minimum execution time. CloudSim tool was used to implement the proposed algorithm where the impact of the algorithm was checked with 5, 10 and 20 VMs besides input tasks and evaluated based on makespan and degree of imbalance metrics. Experimental results showed that for 20 VMs used, our proposed OTB-CSO was able to minimize makespan of the total tasks scheduled across VMs with 42.86, 34.57 and 2.58% improvement over minimum and maximum job first (Min–Max), particle swarm optimization with linear descending inertia weight (PSO-LDIW) and hybrid PSO with simulated annealing (HPSO-SA) and likewise returned degree of imbalance with 70.03, 62.83 and 35.68% improvement over existing algorithms. Results obtained showed that OTB-CSO is effective to optimize task scheduling and improve overall cloud computing performance through minimizing task execution delay while ensuring better system utilization.

Flexible Manufacturing Cell Formation of Processing Workshop based on Intelligent Computing

Flexible manufacturing cell model of multiple workshops is constructed, which combines the advantages of physical manufacturing cell and logical manufacturing cell. A cell formation method based on adaptive inertia weight PSO is worked out, which considers the layout of manufacturing resources, the selection of processing path and the setting of processing batches. The experiment proves that the method can effectively reduce the total manufacturing cost.

Forecasting Logistics Demand Using Unbiased GM (1,1) Model Optimized by AIWPSO Algorithm

Accurate forecast of logistics demand can provide scientific guidance for logistics planning and decision making. With the complexity and uncertainty characteristics in logistics demand, the forecasting of logistics demand shows comprehensive and complex. The forecasting precision of the traditional forecasting methods often are not satisfying. It is necessary to look for novel forecasting methods to enhance the forecasting precision of logistics demand. Integrating the unbiased GM (1,1) model (UGM (1,1)) into the adaptive inertia weight particle swarm optimization (AIWPSO) algorithm, this paper developed a novel model for forecasting logistics demand, called AIWPSO-UGM (1,1) model, in which the UGM (1,1) model was used to forecast logistics demand and the AIWPSO algorithm was adopted to optimize the grey parameters needed in UGM (1,1) model. Two examples were selected to prove the out-of-sample performance of the AIWPSO-UGM (1,1) model in forecasting logistics demand. The results imply that the proposed AIWPSO-UGM (1,1) model performs better in logistics demand forecasting compared to the GM (1,1) model optimized by AIWPSO algorithm (AIWPSO-GM (1,1)), UGM (1,1), and GM (1,1) models.

Monkey King Evolution: an enhanced ebb-tide-fish algorithm for global optimization and its application in vehicle navigation under wireless sensor network environment

Optimization algorithms are proposed to maximize the desirable properties while simultaneously minimizing the undesirable characteristics. Particle Swarm Optimization (PSO) is a famous optimization algorithm, and it has undergone many variants since its inception in 1995. Though different topologies and relations among particles are used in some state-of-the-art PSO variants, the overall performance on high dimensional multimodal optimization problem is still not very good. In this paper, we present a new memetic optimization algorithm, named Monkey King Evolutionary (MKE) algorithm, and give a comparative view of the PSO variants, including the canonical PSO, Inertia Weighted PSO, Constriction Coefficients PSO, Fully-Informed Particle Sawrm, Cooperative PSO, Comprehensive Learning PSO and some variants proposed in recent years, such as Dynamic Neighborhood Learning PSO, Social Learning Particle Swarm Optimization etc. The proposed MKE algorithm is a further work of ebb-tide-fish algorithm and what's more it performs very well not only on unimodal benchmark functions but also on multimodal ones on high dimensions. Comparison results under CEC2013 test suite for real parameter optimization show that the proposed MKE algorithm outperforms state-of-the-art PSO variants significantly. An application of the vehicle navigation optimization is also discussed in the paper, and the conducted experiment shows that the proposed approach to path navigation optimization saves travel time of real-time traffic navigation in a micro-scope traffic networks.

AN ADAPTIVE LOCALIZATION SYSTEM USING PARTICLE SWARM OPTIMIZATION IN A CIRCULAR DISTRIBUTION FORM

Tracking the user location in indoor environment becomes substantial issue in recent research High accuracy and fast convergence are very important issues for a good localization system. One of the techniques that are used in localization systems is particle swarm optimization (PSO). This technique is a stochastic optimization based on the movement and velocity of particles. In this paper, we introduce an algorithm using PSO for indoor localization system. The proposed algorithm uses PSO to generate several particles that have circular distribution around one access point (AP). The PSO generates particles where the distance from each particle to the AP is the same distance from the AP to the target. The particle which achieves correct distances (distances from each AP to target) is selected as the target. Four PSO variants, namely standard PSO (SPSO), linearly decreasing inertia weight PSO (LDIW PSO), self-organizing hierarchical PSO with time acceleration coefficients (HPSO-TVAC), and constriction factor PSO (CFPSO) are used to find the minimum distance error. The simulation results show the proposed method using HPSO-TVAC variant achieves very low distance error of 0.19 meter.

Forecasting Range Volatility Using Support Vector Machines with Improved PSO Algorithms

Financial volatility forecasting is an important content in financial derivatives pricing, financial risk management, portfolio allocation. Support vector machines (SVM) has been successfully used in modeling and forecasting volatility. To enhance the volatility forecasting ability of SVM, this paper introduced three improved particle swarm optimization (IPSO) algorithms into SVM for forecasting range volatility. SVM was used to construct the volatility forecasting model. And, the three IPSO algorithms: stochastic inertia weight PSO (SIWPSO), time-varying acceleration coefficients PSO (TVACPSO) and two-order oscillating particle swarm optimization (TOOPSO) were applied to finding the optimal parameters in SVM, respectively. The accuracy of the proposed models in forecasting range volatility was demonstrated by using three stock indices in China stock market. The empirical results imply that compared to SVM with SIWPSO and TVACPSO algorithms, SVM with TOOPSO algorithm produces higher accuracy in forecasting range volatility and faster speed on searching for the optimal parameters of SVM.

A Novel Flexible Inertia Weight Particle Swarm Optimization Algorithm.

Particle swarm optimization (PSO) is an evolutionary computing method based on intelligent collective behavior of some animals. It is easy to implement and there are few parameters to adjust. The performance of PSO algorithm depends greatly on the appropriate parameter selection strategies for fine tuning its parameters. Inertia weight (IW) is one of PSO’s parameters used to bring about a balance between the exploration and exploitation characteristics of PSO. This paper proposes a new nonlinear strategy for selecting inertia weight which is named Flexible Exponential Inertia Weight (FEIW) strategy because according to each problem we can construct an increasing or decreasing inertia weight strategy with suitable parameters selection. The efficacy and efficiency of PSO algorithm with FEIW strategy (FEPSO) is validated on a suite of benchmark problems with different dimensions. Also FEIW is compared with best time-varying, adaptive, constant and random inertia weights. Experimental results and statistical analysis prove that FEIW improves the search performance in terms of solution quality as well as convergence rate.

Prediction of Military Vehicle's Drawbar Pull Based on an Improved Relevance Vector Machine and Real Vehicle Tests.

The scientific and effective prediction of drawbar pull is of great importance in the evaluation of military vehicle trafficability. Nevertheless, the existing prediction models have demonstrated lots of inherent limitations. In this framework, a multiple-kernel relevance vector machine model (MkRVM) including Gaussian kernel and polynomial kernel is proposed to predict drawbar pull. Nonlinear decreasing inertia weight particle swarm optimization (NDIWPSO) is employed for parameter optimization. As the relations between drawbar pull and its influencing factors have not been tested on real vehicles, a series of experimental analyses based on real vehicle test data are done to confirm the effective influencing factors. A dynamic testing system is applied to conduct field tests and gain required test data. Gaussian kernel RVM, polynomial kernel RVM, support vector machine (SVM) and generalized regression neural network (GRNN) are also used to compare with the MkRVM model. The results indicate that the MkRVM model is a preferable model in this case. Finally, the proposed novel model is compared to the traditional prediction model of drawbar pull. The results show that the MkRVM model significantly improves the prediction accuracy. A great potential of improved RVM is indicated in further research of wheel-soil interactions.

FOPID controller optimization based on SIWPSO-RBFNN algorithm for fractional-order time delay systems

In this study, the stochastic inertia weight particle swarm optimization (SIWPSO) algorithm and radial basis function neural network (RBFNN) methods were used to identify the optimal controller gain for the fractional order proportional integral derivative (FOPID) controller of time-delay systems; furthermore, a graphic approach was used to plot 3D stability regions in the $$k_p $$kp, $$k_i $$ki, and $$k_d$$kd parameter space. This paper presents an intelligent SIWPSO-RBF algorithm for identifying the optimal solution for a FOPID control system. To explain how to use the SIWPSO-RBFNN method, this paper presents two cases describing how the proposed algorithm can be useful in FOPID-type controllers with two fractional-order time-delay systems. Furthermore, the proposed algorithm can be used in two desired procedures if the system transfer functions are known. The first procedure involves identifying the optimal $$k_p$$kp and $$k_i$$ki gains while $$k_d $$kd varies and the parameters $$\lambda $$ź and $$\mu $$μ are known. The second procedure involves identifying the optimal $$k_p $$kp, $$k_i $$ki and $$k_d$$kd gains while $$\lambda $$ź and $$\mu $$μ vary. Finally, several simulations of the proposed algorithm verified the effectiveness of a FOPID controller regarding fractional-order with time-delay systems.

Grasping Posture Control Design for a Home Service Robot Using an ABC-Based Adaptive PSO Algorithm

In this paper, a grasping posture control for a robotic arm is developed based on novel adaptive particle swarm optimization (PSO) for the home service robot. To grasp an object using the robotic arm of the home-service robot, both the spatial coordinates of the target and the appropriate collocation of the grasping posture should be examined. In this paper, we present another method for dealing with this problem, which integrates the artificial bee colony (ABC) algorithm into the adaptive particle swarm optimization (APSO) algorithm, where the mutation concept of the scout bee in the ABC algorithm is used to increase the diversity of the particles. In addition, adaptive acceleration coefficients and adaptive inertia weight are presented to ameliorate the convergence rate of the PSO algorithm. We name this control scheme AIWCPSO-S, which represents Adaptive Inertia Weight and acceleration Coefficients PSO with the aid of the Scout bee. Performance comparisons of existing ABC, global ABC, adaptive inertia weight PSO, low-discrepancy sequence initialized PSO algorithm with high-order nonlinear time-varying inertia weight (LHNPSO), oscillating triangular inertia weight PSO (OTIWPSO) and AIWCPSO-S algorithms are conducted by computer simulations. The experiment results show that the presented algorithm gives the most correct and fastest convergence capability.

Dynamic Inertia Weight Particle Swarm Optimization for Solving Nonogram Puzzles

Particle swarm optimization (PSO) has shown to be a robust and efficient optimization algorithm therefore PSO has received increased attention in many research fields. This paper demonstrates the feasibility of applying the Dynamic Inertia Weight Particle Swarm Optimization to solve a Non-Polynomial (NP) Complete puzzle. This paper presents a new approach to solve the Nonograms Puzzle using Dynamic Inertia Weight Particle Swarm Optimization (DIW-PSO). We propose the DIW-PSO to optimize a problem of finding a solution for Nonograms Puzzle. The experimental results demonstrate the suitability of DIW-PSO approach for solving Nonograms puzzles. The outcome results show that the proposed DIW-PSO approach is a good promising DIW-PSO for NP-Complete puzzles.

Impact of Learning Algorithms on Random Neural Network based Optimization for LTE-UL Systems

This paper presents an application of context-aware decision making to the problem of radio resource management (RRM) and inter-cell interference coordination (ICIC) in long-term evolution-uplink (LTE-UL) system. The limitations of existing analytical, artificial intelligence (AI), and machine learning (ML) based approaches are highlighted and a novel integration of random neural network (RNN) based learning with genetic algorithm (GA) based reasoning is presented. In first part of the implementation, three learning algorithms (gradient descent (GD), adaptive inertia weight particle swarm optimization (AIWPSO), and differential evolution (DE)) are applied to RNN and two learning algorithms (GD and levenberg-marquardt (LM)) are applied to artificial neural network (ANN). In second part of the implementation, the GA based reasoning is applied to the trained ANN and RNN models for performance optimization. Finally, the ANN and RNN based optimization results are compared with the state-of-the-art fractional power control (FPC) schemes in terms of user throughput and power consumption. The simulation results have revealed that an RNN-DE (RNN trained with DE algorithm) based cognitive engine (CE) can provide up to 14% more cell capacity along with 6dBm and 9dBm less user power consumption as compared to RNN-GD (RNN trained with GD algorithm) and FPC methods respectively.

Empirical Analysis of Effect of Particle Swarm Optimization Inertia Weight Strategies over Particle Swarm Optimization with Aging Leader and Challengers

Empirical Analysis of Effect of Particle Swarm Optimization Inertia Weight Strategies over Particle Swarm Optimization with Aging Leader and Challengers

Coalition Formation in Multi-agent Systems Based on Improved Particle Swarm Optimization Algorithm

How to generate the task-oriented optimal agent coalition is a key issue of multi-agent system, which is a typical optimization problem. In this paper, an improved particle swarm optimization (IPSO) is proposed to solve this problem. In order to overcome the premature and local optimization problem in traditional particle swarm optimization (PSO), we proposed a variation of inertia weight PSO algorithm by analyzing the feasibility of particle optimization process in PSO. Compared with several well-known algorithms such as PSO, ACO, experimental results show that the global search capability of IPSO has been significantly improved and IPSO can effectively avoid premature convergence problem. Also it can solve the multi-agent coalition formation problem effectively and efficiently.

Fault Diagnosis of Bearing Based on Cauchy Kernel Relevance Vector Machine Classifier with SIWPSO

Bearing is an important component of mechanical system; any defects of bearing will lead to serious damage for the entire mechanical system. In this paper, Cauchy kernel relevance vector machine with stochastic inertia weight particle swarm optimization algorithm (SIWPSO-CauchyRVM) is proposed to fault diagnosis for bearing. As the selection of the Cauchy kernel parameter has a certain influence on the diagnosis result of relevance vector machine, stochastic inertia weight PSO is used to select the Cauchy kernel parameter. The relative energies of 16 wavelet coefficients of the forth layer of vibration signal of bearing can be used as the diagnosis features of bearing. The experimental results indicate that fault diagnosis method of bearing based on SIWPSO-CauchyRVM has excellent diagnosis ability.

Parameter Estimation in Rainfall-Runoff Modelling Using Distributed Versions of Particle Swarm Optimization Algorithm

The presented paper provides the analysis of selected versions of the particle swarm optimization (PSO) algorithm. The tested versions of the PSO were combined with the shuffling mechanism, which splits the model population into complexes and performs distributed PSO optimization. One of them is a new proposed PSO modification, APartW, which enhances the global exploration and local exploitation in the parametric space during the optimization process through the new updating mechanism applied on the PSO inertia weight. The performances of four selected PSO methods were tested on 11 benchmark optimization problems, which were prepared for the special session on single-objective real-parameter optimization CEC 2005. The results confirm that the tested new APartW PSO variant is comparable with other existing distributed PSO versions, AdaptW and LinTimeVarW. The distributed PSO versions were developed for finding the solution of inverse problems related to the estimation of parameters of hydrological model Bilan. The results of the case study, made on the selected set of 30 catchments obtained from MOPEX database, show that tested distributed PSO versions provide suitable estimates of Bilan model parameters and thus can be used for solving related inverse problems during the calibration process of studied water balance hydrological model.

Dynamic Economic Dispatch for Combined Heat and Power Units using Particle Swarm Algorithms

In this paper, combined heat and power units are incorporated in dynamic economic dispatch to minimize total production costs considering realistic constraints such as ramp rate and spinning reserve limits effects over a short time span. Four evolutionary approaches, namely particle swarm optimization (PSO), particle swarm optimization with constriction factor (PSOCFA), particle swarm optimization with inertia weight factor (PSOIWA) and particle swarm optimization with both constriction factor and inertia weight factor (PSOCFIWA) are successfully implemented to solve the combined heat and power economic dispatch (CHPED) problem. These approaches have been tested on 12-generation units system with two steam, four gas and six cogeneration units. In addition, the performance tests are applied to measure the actual power output and the fuel consumption in every point tests for achieving different curves such as input/output, incremental heat rate and heat rate curves for the twelve units. The results of the four approaches are compared with those obtained using existing performance testing method. The results show that the particle swarm optimization with improved inertia weight is able to achieve a better solution at less computational time.

A Multiple-Kernel Relevance Vector Machine with Nonlinear Decreasing Inertia Weight PSO for State Prediction of Bearing

The scientific and accurate prediction for state of bearing is the key to ensure its safe operation. A multiple-kernel relevance vector machine (MkRVM) including RBF kernel and polynomial kernel is proposed for state prediction of bearing in this study; the proportions of RBF kernel and polynomial kernel are determined by a controlled parameter. As the selection of the parameters of the kernel functions and the controlled parameter has a certain influence on the prediction results of MkRVM, nonlinear decreasing inertia weight PSO (NDIWPSO) is used to select its kernel parameters and controlled parameter. The RBF kernel RVM model with NDIWPSO (NDIWPSO-RBFRVM) and the polynomial kernel RVM model with NDIWPSO (NDIWPSO-PolyRVM) are used, respectively, to compare with the multiple-kernel RVM model with NDIWPSO (NDIWPSO-MkRVM). The experimental results indicate that NDIWPSO-MkRVM is more suitable for the state prediction of bearing than NDIWPSO-RBFRVM and NDIWPSO-PolyRVM.