Fitness Function Of Particle Swarm Optimization Research Articles

An optimized microstrip antenna to generate intense localized heating at target sites for maximum effect

AbstractNowadays, the use of electromagnetic waves in medical applications has become common, and hyperthermia is one of the popular areas. Nonetheless, designing effective antennas for electromagnetic hyperthermia poses a key challenge. In designing of hyperthermia antennas for medical applications, factors such as appropriate resonant frequencies and appropriate antenna sizes are important. Another critical aspect in the design of useful and usable hyperthermia antenna is the heat on the target body area, since a proper depth setting for heating is normally neglected. In this paper, using the Particle Swarm Optimization (PSO) algorithm, we focus on the heat on the target area when designing a hyperthermia antenna that operates at the frequency of 432 MHz. The antenna is analyzed using the finite difference time domain method, while the PSO fitness function is selected in such a way as to maintain the optimal frequency characteristics of the antenna, along with optimization of its heating performance. A series of simulation studies in MATLAB and the associated laboratory results confirm accuracy of the designed antenna. With minimal influence on healthy tissues, the temperature of approximately 42 degrees Celsius is achieved steadily after about 12 min from the start of heating in the target area, where the tumor is located. We observe minor differences between simulation and laboratory results, owing to not being able to use living tissue in the laboratory and lack of precision in the construction of antenna with optimized parameters.

Research on optimization of aiming point of sub cluster anti-airport runway based on particle swarm optimization algorithm

A particle swarm optimization algorithm is proposed in this study to optimize the fire distribution, so as to solve the fire distribution problem of cluster warhead anti-airport runway. The particle swarm optimization fitness function is built based on the calculation model of cluster warhead anti-airport runway blocking probability using the Monte Carlo method, and the optimal cluster warhead aiming point allocation scheme is obtained using the particle swarm optimization algorithm. Moreover, the feasibility and advantages of the algorithm are compared and analyzed in the case of different size airports and different number of submunitions. The results provide a reference for the formulation of operational scheme when the cluster bomb strikes the airport runway.

Mitigation of Power Losses and Enhancement in Voltage Profile by Optimal Placement of Capacitor Banks With Particle Swarm Optimization in Radial Distribution Networks

The prime purpose of placing a capacitor bank in a power system is to provide reactive power, reduce power losses, and enhances voltage profile. The main challenge is to determine the optimum capacitor position and size that reduces both system power losses and the overall cost of the system with rigid constraints. For this purpose, different optimization techniques are used, for example Particle Swarm Optimization (PSO) which converges the complex non-linear problem in a systematic and methodological way to find the best optimal solution. In this paper, the standard IEEE 33-bus and 69-bus systems are used to find the optimum location and size of the capacitors bank. These power networks are simulated in Siemens PSS®E software. For the optimum solution of capacitor banks, the PSO algorithm is used. The PSO fitness function is modelled in such a way which contains the high average bus voltage, the small size of capacitor banks, and low power losses. The fitness function used is a weighted type to reduce the computation time and multi-objective function complexity.

Particle Swarm Optimization for Dynamic Risk-Aware Path Following for Autonomous Ships

This work presents the use of particle swarm optimization (PSO) for dynamic risk-aware path following, or waypoint re-planning during autonomous surface navigation in a maritime environment with polygonal grounding obstacles. Although recent research on control and local or global path planning for maritime autonomous surface ships (MASS) is considerable, few deal with the concept of risk during dynamic path following along a preplanned path. The proposed method introduces risk-based terms in the PSO fitness function for dynamic (online) adjustment or re-planning of intermediate waypoints during path following of a pre-planned route, where the control of the vessel in this work is left to a standard line-of-sight PID controller. Moreover, the results are compared to the performance of an analogous implementation of risk-aware model predictive control (MPC) using a gradient-based solver. The suggested method yields adequate performance similar to that of the MPC algorithm. Ultimately, the PSO approach may in future works allow for more complex and dynamic risk-aware behavior related to e.g. weather conditions implemented through lookup tables, or discrete machinery modes that are non-smooth.

Experimental Investigation for the Multi-objective Optimization of Machining Parameters on AISI D2 Steel Using Particle Swarm Optimization Coupled with Artificial Neural Network

High Carbon High Chromium (or AISI D2) Steels, owing to the fine surface finish they produce upon grinding, find lot of applications in die casting. Machining parameters affect the surface finish significantly during the grinding operation. In this context, this work puts an effort to arrive at the optimum machining parameters relating to fine surface finish with minimum cutting force. The material removal caused by the abrasive grinding wheel makes the process a very complex and nonlinear machining operation. In many situations, traditional optimization techniques fail to provide realistic optimum conditions because of the associated complexity. In order to overcome this issue, particle swarm optimization (PSO) coupled with artificial neural network (ANN) is applied in this research work for parameter optimization with the objective of achieving minimum surface roughness and cutting force. The machining parameters selected for the investigation were table speed, cross feed and depth of cut and the responses were surface roughness and cutting force. ANNs, inspired from biological neural networks, are well capable of providing patterns, which are too complex in behavior. The ANN model developed was used as the fitness function for PSO to complete the optimization. Optimization was also carried out using conventional response surface methodology-genetic algorithm (RSM-GA) approach in which regression equation developed with RSM was considered as the fitness function for GA. Confirmatory experiments were conducted and the comparison showed that PSO coupled with ANN is a reliable tool for complex optimization problems.

An Improved Lagrange Particle Swarm Optimization Algorithm and Its Application in Multiple Fault Diagnosis

The fault rate in equipment increases significantly along with the service life of the equipment, especially for multiple fault. Typically, the Bayesian theory is used to construct the model of faults, and intelligent algorithm is used to solve the model. Lagrangian relaxation algorithm can be adopted to solve multiple fault diagnosis models. But the mathematical derivation process may be complex, while the updating method for Lagrangian multiplier is limited and it may fall into a local optimal solution. The particle swarm optimization (PSO) algorithm is a global search algorithm. In this paper, an improved Lagrange-particle swarm optimization algorithm is proposed. The updating of the Lagrangian multipliers is with the PSO algorithm for global searching. The difference between the upper and lower bounds is proposed to construct the fitness function of PSO. The multiple fault diagnosis model can be solved by the improved Lagrange-particle swarm optimization algorithm. Experiment on a case study of sensor data-based multiple fault diagnosis verifies the effectiveness and robustness of the proposed method.

Optimal Design of Conical Horn Antenna Based on GP Model with Coarse Mesh

Gauss process (GP) is a learning machine which has developed rapidly in recent years. Compared with the methods of artificial neural network and support vector machine, GP is easy to implement and has the advantages of adaptive acquisition and predictive output. This paper presents a modeling method based on GP. When constructing the proposed GP model, the input samples of the model calculates the results of electromagnetic simulation software with coarse mesh, and the corresponding output samples are these of electromagnetic simulation software with precise mesh. Based on the proposed GP model exploiting particle swarm optimization (PSO) algorithm, a dual-mode conical horn antenna is optimized, and the optimization results are perfect. The modeling process and computing results show that the proposed GP model based on the coarse mesh can greatly reduce the mapping relation between the input and output, and its generalization ability is excellent. Moreover, in the optimization process, the trained GP model can be used to evaluate the fitness function of PSO, and the time required for optimization is obviously reduced because it can give the fitness function value rapidly.

Particle Swarm Optimization-Based Gyro Drift Estimation Method for Inertial Navigation System

This paper proposes a novel gyro drift estimation method for the inertial navigation system (INS), which introduces the particle swarm optimization (PSO) algorithm into the error estimation problem. PSO-based estimation model is established. The position error and velocity error of INS are considered as the performance criterions of the PSO fitness function. Compared with the traditional gyro drift estimation methods, the advantages or contributions of the proposed method can be summarized as follows: 1) the proposed method does not require any prior information about inertial sensor error or observation noise; 2) particular motion for the carrier of INS is not needed, and; 3) the external information provided by other navigation systems could be discontinuous. The simulation experiments and field tests are performed, which validate the efficacy of the proposed method.

A Machine Learning Framework for Feature Selection in Heart Disease Classification Using Improved Particle Swarm Optimization with Support Vector Machine Classifier

Machine learning is used as an effective support system in health diagnosis which contains large volume of data. More commonly, analyzing such a large volume of data consumes more resources and execution time. In addition, all the features present in the dataset do not support in achieving the solution of the given problem. Hence, there is a need to use an effective feature selection algorithm for finding the more important features that contribute more in diagnosing the diseases. The Particle Swarm Optimization (PSO) is one of the metaheuristic algorithms to find the best solution with less time. Nowadays, PSO algorithm is not only used to select the more significant features but also removes the irrelevant and redundant features present in the dataset. However, the traditional PSO algorithm has an issue in selecting the optimal weight to update the velocity and position of the particles. To overcome this issue, this paper presents a novel function for identifying optimal weights on the basis of population diversity function and tuning function. We have also proposed a novel fitness function for PSO with the help of Support Vector Machine (SVM). The objective of the fitness function is to minimize the number of attributes and increase the accuracy. The performance of the proposed PSO-SVM is compared with the various existing feature selection algorithms such as Info gain, Chi-squared, One attribute based, Consistency subset, Relief, CFS, Filtered subset, Filtered attribute, Gain ratio and PSO algorithm. The SVM classifier is also compared with several classifiers such as Naive Bayes, Random forest and MLP.

Pilot tones design using particle swarm optimization for OFDM–IDMA system

Since the channel estimation performance is directly affected by the pilot positions, it has been a major task in multicarrier transmission schemes to adjust the pattern of pilot distribution for the purpose of minimizing the estimation errors. Therefore, in this paper, we utilize particle swarm optimization (PSO) algorithm for pilot design process in orthogonal frequency division multiplexing–interleave division multiple access (OFDM–IDMA) system. The main contributions of the paper are: (1) increasing the performance of least squares (LS) algorithm used for channel estimation in OFDM–IDMA system by optimizing the pilot positions using PSO algorithm, (2) instead of using MSE itself in which the matrix inversion process is required as the fitness function of PSO, using the upper bound of mean square error (MSE) in order to decrease the system complexity, (3) using two types of channel models known as COST 207 Rural Area and COST 207 Typical Urban in the simulations to be able to support the reliability and stability of the proposed method in different conditions. In the simulations, it is observed from the bit error rate (BER) and MSE graphs that optimizing the placement of pilot tones using our proposed method demonstrates a superior performance compared to the other considered pilot placement strategies by providing a significant increase in the performance of LS algorithm.

Fuzzy mutual information based grouping and new fitness function for PSO in selection of miRNAs in cancer

MicroRNAs (miRNA) are one of the important regulators of cell division and also responsible for cancer development. Among the discovered miRNAs, not all are important for cancer detection. In this regard a fuzzy mutual information (FMI) based grouping and miRNA selection method (FMIGS) is developed to identify the miRNAs responsible for a particular cancer. First, the miRNAs are ranked and divided into several groups. Then the most important group is selected among the generated groups. Both the steps viz., ranking of miRNAs and selection of the most relevant group of miRNAs, are performed using FMI. Here the number of groups is automatically determined by the grouping method. After the selection process, redundant miRNAs are removed from the selected set of miRNAs as per user's necessity. In a part of the investigation we proposed a FMI based particle swarm optimization (PSO) method for selecting relevant miRNAs, where FMI is used as a fitness function to determine the fitness of the particles. The effectiveness of FMIGS and FMI based PSO is tested on five data sets and their efficiency in selecting relevant miRNAs are demonstrated. The superior performance of FMIGS to some existing methods are established and the biological significance of the selected miRNAs is observed by the findings of the biological investigation and publicly available pathway analysis tools. The source code related to our investigation is available at http://www.jayanta.droppages.com/FMIGS.html.

Study on Nonlinear Identification SOFC Temperature Model Based on Particle Swarm Optimization–Least-Squares Support Vector Regression

In order to facilitate valid solid oxide fuel cell (SOFC) temperature control scheme, a nonlinear identification method of SOFC temperature dynamic behaviors is proposed using an autoregressive network with exogenous inputs (NARX) model, whose nonlinear function is described by a least-squares support vector regression (LSSVR) method with radial basis kernel function (RBF). During the identifying process, a particle swarm optimization (PSO) algorithm is introduced to optimize the parameters of LSSVR. On the other hand, a mechanism model is developed to sample the training data to regress the NARX model. Investigations are conducted to analyze the effects of training data size and PSO fitness function on the accuracy of the NARX model. The results demonstrate that the NARX model with tenfold cross-validation fitness function and large size data is precise enough in predicting the SOFC temperature dynamic behaviors. The maximum errors of cathode and anode outlet temperature are 0.3081 K and 0.3293 K, respectively. Furthermore, the simulation speed of NARX model is much faster than the mechanism model because NARX model avoids the internal complex computation process. The training time of the NARX model with large size data is about 1.2 s. For a 20,000 s simulation, the predicting time of the NARX model is about 0.2 s, while the mechanism model is about 36 s. In consideration of its high computational speed and accuracy, NARX model is a powerful candidate for valid multivariable model predictive control (MPC) schemes.

Particle Swarm Optimization approach to defect detection in armour ceramics

In this research, various extracted features were used in the development of an automated ultrasonic sensor based inspection system that enables defect classification in each ceramic component prior to despatch to the field. Classification is an important task and large number of irrelevant, redundant features commonly introduced to a dataset reduces the classifiers performance. Feature selection aims to reduce the dimensionality of the dataset while improving the performance of a classification system. In the context of a multi-criteria optimization problem (i.e. to minimize classification error rate and reduce number of features) such as one discussed in this research, the literature suggests that evolutionary algorithms offer good results. Besides, it is noted that Particle Swarm Optimization (PSO) has not been explored especially in the field of classification of high frequency ultrasonic signals. Hence, a binary coded Particle Swarm Optimization (BPSO) technique is investigated in the implementation of feature subset selection and to optimize the classification error rate. In the proposed method, the population data is used as input to an Artificial Neural Network (ANN) based classification system to obtain the error rate, as ANN serves as an evaluator of PSO fitness function.

Optimized Gabor features for mass classification in mammography

Gabor filter bank has been successfully used for false positive reduction problem and the discrimination of benign and malignant masses in breast cancer detection. However, a generic Gabor filter bank is not adapted to multi-orientation and multi-scale texture micro-patterns present in the regions of interest (ROIs) of mammograms. There are two main optimization concerns: how many filters should be in a Gabor filter band and what should be their parameters. Addressing these issues, this work focuses on finding optimizing Gabor filter banks based on an incremental clustering algorithm and Particle Swarm Optimization (PSO). We employ an SVM with Gaussian kernel as a fitness function for PSO. The effect of optimized Gabor filter bank was evaluated on 1024 ROIs extracted from a Digital Database for Screening Mammography (DDSM) using four performance measures (i.e., accuracy, area under ROC curve, sensitivity and specificity) for the above mentioned mass classification problems. The results show that the proposed method enhances the performance and reduces the computational cost. Moreover, the Wilcoxon signed rank test over the significance level of 0.05 reveals that the performance difference between the optimized Gabor filter bank and non-optimized Gabor filter bank is statistically significant.

Integral Performance Criteria Based Analysis of Load Frequency Control in Bilateral Based Market

Performance index based analysis is made to examine and highlight the effective application of Particle Swarm Optimization (PSO) to optimize the Proportional Integral gains for Load Frequency Control (LFC) in a restructured power system that operates under Bilateral based policy scheme. Various Integral Performance Criteria measures are taken as fitness function in PSO and are compared using overshoot, settling time and frequency and tie-line power deviation following a step load perturbation (SLP). The motivation for using different fitness technique in PSO is to show the behavior of the controller for a wide range of system parameters and load changes. Error based analysis with parametric uncertainties and load changes are tested on a two-area restructured power system. The results of the proposed PSO based controller show the better performance compared to the classical Ziegler-Nichols (Z-N) tuned PI and Fuzzy Rule based PI controller.

Improving Energy and Network Lifetime using PSO based Apriori in Wireless Sensor Networks

In WSN, the sensor nodes can be placed in any area in the environment. The main work of sensor node is to sense and collect data for forwarding data to a base station or sink. The Cluster formation with load balanced and Effective routing is the key issue in WSN. In this study the problem is to be overcome the cluster formation by using the SEARCH (Sustainability, Energy Aware Cluster Head) protocol, then routing is done by using the PSO (Particle Swarm Optimization) Multi-objective Fitness Function with Apriori Algorithm and its Association rules. The SEARCH protocol helps to detect the energy level of each node in the WSN and helps to form Energy based Clustering to balance the load. The PSO Fitness Function and Apriori Association rules, helps to route the sensed data using efficient routing, by taking the amount of data transfer and the energy efficiency of all routes in order to maintain the energy for long term transmission. As a result, here we implemented this work in java platform. This will shows the better performance of nodes in the network. It will increase the lifetime of network and energy efficiency of the node.

Particle Swarm Optimization Based Technique for Deposition Parameters Optimization of Magnetron Sputtering Process

This study presents a soft computing based technique in the deposition parameters optimization of RF Magnetron Sputtering process. Particle Swarm Optimization (PSO) has been chosen due to its good performance in solving various optimization problems. The material used in this study was zinc oxide (ZnO) and there were four deposition parameters involved the optimization process. The deposition parameters were RF power, deposition time, oxygen flow rate and substrate temperature. The aim of the study was to obtain the optimal combination for the selected deposition parameters in order to produce the desirable ZnO thin film properties. In this study, the Desirability Function had been adapted as the fitness function for PSO. Desirability function is one of the commonly used statistical method for obtaining optimal process parameter design. The result from the PSO based optimization technique was then compared with actual laboratory result. Based on the observation made, the PSO based technique has been proven to be reliable and satisfactory in obtaining the optimal deposition parameters of ZnO thin film. It is expected that this soft computing based technique for optimizing the deposition parameters could reduce the trial and error method before the experiment is conducted in the fabrication process.

Energy Efficient Multiresource Allocation of Virtual Machine Based on PSO in Cloud Data Center

Presently, massive energy consumption in cloud data center tends to be an escalating threat to the environment. To reduce energy consumption in cloud data center, an energy efficient virtual machine allocation algorithm is proposed in this paper based on a proposed energy efficient multiresource allocation model and the particle swarm optimization (PSO) method. In this algorithm, the fitness function of PSO is defined as the total Euclidean distance to determine the optimal point between resource utilization and energy consumption. This algorithm can avoid falling into local optima which is common in traditional heuristic algorithms. Compared to traditional heuristic algorithms MBFD and MBFH, our algorithm shows significantly energy savings in cloud data center and also makes the utilization of system resources reasonable at the same time.

Research of Hierarchical Intrusion Detection Model Based on Discrete Cellular Neural Networks

In this paper, a new method of hierarchical intrusion detection algorithm based on the Discrete Cellular Neural Networks (HDCNN) is put forward to solve the problems of low accuracy and slow speed in the existing intrusion detection algorithm. In order to obtain the template parameters for the HDCNN classifier, we use energy function constraint method to construct a new particle swarm optimization fitness function, jumping out the premature convergence. Emerging evidence has indicated that this new approach is affordable to parallelism and analog VLSI implementation. Experimental results and comparative studies based on the KDD cup 99 data sets are given, show that the proposed model exhibits an excellent performance owing to the higher attack detection rate and shorter processing time.

Segmentation of histology slides of cortical bone using pulse coupled neural networks optimized by particle-swarm optimization

The aim of this study is to automatically discern the micro-features in histology slides of cortical bone using pulse coupled neural networks (PCNN). To the best knowledge of the authors, utilizing PCNN in such an application has not been reported in the literature and, as such, constitutes a novel application. The network parameters are optimized using particle swarm optimization (PSO) where the PSO fitness function was introduced as the entropy and energy of the bone micro-constituents extracted from a training image. Another novel contribution is combining the above with the method of adaptive threshold (T) where the PCNN algorithm is repeated until the best threshold T is found corresponding to the maximum variance between two segmented regions. To illustrate the quality of resulting segmentation according to this methodology, a comparison of the entropy/energy obtained of each pulse is reported. Suitable quality metrics (precision rate, sensitivity, specificity, accuracy, and dice) were used to benchmark the resulting segments against those found by a more traditional method namely K-means. The quality of the segments revealed by this methodology was found to be of much superior quality. Another testament to the quality of this methodology was that the images resulting from testing pulses were found to be of similarly good quality to those of the training images.