Orthogonal Particle Swarm Optimization Research Articles

Multi-response Characteristics Optimization of a Paper-based Composite using Taguchi Method and Particle Swarm Optimization

Our dependency towards wood has reduced the number of forests especially in Indonesia. Hence, many people tried to use another material as a replacement of wood. One of them is composite. Department of Industrial Engineering Universitas Sebelas Maret has developed paper-based composite using newspaper waste in Praktikum Perancangan Teknik Industri (PPTI) II. Unfortunately, the quality of composite is not so good. This paper tried to understand the effect of factors to responses and discover the best combination of factors to obtain the best responses. The responses of this experiment are tensile strength, bending strength and impact value. The factors of this experiment are paper pulp, PVAc glue and water. Taguchi method is used to find the appropriate orthogonal array and Particle Swarm Optimization method to find the optimum combination of the factors. Orthogonal array used in this experiment is L9 (34). Through analysis, all of the factors are significant to tensile and bending strength, but they are not significant to impact value. The best combination of factors is 175 grams paper pulp, 130 grams PVAc glue and 175 ml water. Optimum results obtained for tensile strength is 0.7595 N/mm2, bending strength is 4.873 N/mm2 and impact value is 0.0138 J/mm2.

An intelligent internet of things-based secure healthcare framework using blockchain technology with an optimal deep learning model

Today, the internet of things (IoT) is becoming more common and finds applications in several domains, especially in the healthcare sector. Due to the rising demands of IoT, a massive quantity of sensing data gets generated from diverse sensing devices. Artificial intelligence (AI) techniques are vital for providing a scalable and precise analysis of data in real time. But the design and development of a useful big data analysis technique face a few challenges, like centralized architecture, security, and privacy, resource constraints, and the lack of adequate training data. On the other hand, the rising blockchain technology offers a decentralized architecture. It enables secure sharing of data and resources to the different nodes of the IoT network and is promoted for removing centralized control and resolving the problems of AI. This study develops an optimal deep-learning-based secure blockchain (ODLSB) enabled intelligent IoT and healthcare diagnosis model. The proposed model involves three major processes: secure transaction, hash value encryption, and medical diagnosis. The ODLSB technique comprises the orthogonal particle swarm optimization (OPSO) algorithm for the secret sharing of medical images. In addition, the hash value encryption process takes place using neighborhood indexing sequence (NIS) algorithm. At last, the optimal deep neural network (ODNN) is applied as a classification model to diagnose the diseases. The utilization of OPSO algorithm for secret sharing and optimal parameter tuning process shows the novelty of the work. We carried out detailed experiments to validate the outcome of the proposed method, and several aspects of the results are considered. At the time of the diagnosis process, the OPSO-DNN model has yielded superior results, with the highest sensitivity (92.75%), specificity (91.42%), and accuracy (93.68%).

Dynamic economic emission dispatch with load dema nd management for the load demand of electric vehicles during crest shaving and valley filling in smart cities environment

A multi-objective problem, namely, dynamic economic emission dispatch is simultaneously solved under several practical equality and inequality operating power constraints, while applying the load demand management (LDM) on 30,000 electric vehicles (EVs) during crest shaving and valley filling (CSVF) regions. A novel algorithm named orthogonal particle swarm optimization (OPSO) is proposed for such a problem. Ten thermal-generating units (TGUs) from power-generating systems (PGSs) in two Case Studies, with and without LDM on the load demand of 30,000 EVs, are tested. The comprehensive analysis results reveal that the quantity of emissions released by the 10 TGUs was remarkable affected and reduced around 4005 kg/day and the operating fuel cost was saved around $409/day when applying the LDM on the load demand of 30,000 EVs in the CSVF regions. This study provides important outcomes about the future operation of PGSs when applying the LDM strategy on a large-scale penetration of EVs in smart cities into a sustainable environment. The study outcomes contribute in making the PGS flexible, economical, environmental, stable, and reliable.

Distribution transformer load behavior, burden, and characteristics of residential consumers: A case study of Baghdad City

The present study focuses on distribution transformer load behavior and characteristics of residential consumers in Baghdad City. Real-data obtained from outputs of electrical load recording equipment and consumer billing data are used. The sample includes three categories of residential consumers from six different areas geographically distributed throughout the city (total of 3000 consumers). A novel algorithm called orthogonal particle swarm optimization is applied to construct a new approach to electricity demand modeling for the city. Then, the quantitative relationship between energy consumption, peak kW demand during peak demand periods (July and August), and annual energy consumption are evaluated. A quantitative relationship between the coincidence factor and N number of consumers is determined. Results of this study have been successfully applied in determining the distribution transformer burden, which can assist design engineers in calculating and setting the number of consumers connected to the distribution transformer. Such outcomes provide useful references for planning, designing, and load demand forecasting for power distribution systems in the future.

Parameters Tuning of Photovoltaic Power Generation System with Static Synchronous Compensator Based on Chaos Orthogonal Particle Swarm Optimization

China is a country rich in solar energy resources, with good natural conditions for the utilization of solar energy. At present, the main application mode of photovoltaic power generation system is grid-connected photovoltaic power generation. In order to maintain the voltage stability of the common connection point (PCC), STATCOM is mainly added to the PCC of photovoltaic power station. It can be regarded that the photovoltaic system and STATCOM constitute a whole and are integrated into the grid, and the unified coordination and optimization of the overall control system becomes particularly important. In this paper, the parameter tuning strategy of PI controller for photovoltaic grid-connected system with STATCOM is studied. Taking the optimal overall performance of DC capacitor voltage and PCC point voltage of photovoltaic inverters as the optimization objective, chaotic orthogonal particle swarm optimization algorithm is used to optimize the parameters of PI controller of the system, and simulation is carried out under the condition of voltage variation to verify the effectiveness of the optimization strategy.

Design of an Efficient Wideband Beamformer using OPSO

Wideband beamforming is a technique which is used to produce frequency invariant beampatterns over a wide signal bandwidth. In this paper, orthogonal particle swarm optimization (OPSO) based wideband beamformer is designed to achieve wideband beamforming. Simulations are carried out for two different scenarios to illustrate the capability of the algorithm for wideband beamforming. The beampatterns obtained from three different methods that are standard Frost algorithm, particle swarm optimization (PSO) and OPSO are compared by various parameters such as level of interference, deviation in interference angle and peak side lobe level. It is shown that by using OPSO, SLL (Sidelobe Level), null depth as well as angle deviation in nulls direction can be reduced as compared to PSO and Frost algorithms.

Parameters tuning of doubly fed induction generator systems with static synchronous compensator based on Chaos orthogonal particle swarm optimization

For Double-Fed Induction Generator (DFIG) wind farm systems with Static Synchronous Compensator (STATCOM), by optimizing multiple proportional integral (PI) controllers parameters of DFIG and STATCOM, the performance of the wind power system is significantly improved and the voltage of point of common coupling (PCC) is quickly restored when a low voltage due to a grid fault. Chaos orthogonal particle swarm optimization is proposed to make the integral time absolute error (ITAE) of active power of DFIG, DC-link voltage of converter of DFIG and voltage of PCC minimum. The orthogonal method is used to get the range of parameters and identify the weight relations between different indicators. And then the chaos algorithm is used to initialize the algorithm. Finally, the particle swarm optimization (PSO) can effectively improve the efficiency of the optimization. Based on MATLAB/Simulink, the simulations of wind farm containing STATCOM incorporated into the infinite system under different conditions both show that the application optimized PI controllers have good dynamic performance, which validates the effectiveness of the optimization algorithm.

Orthogonal PSO algorithm for economic dispatch of thermal generating units under various power constraints in smart power grid

Particle swarm optimization (PSO) algorithm has been successfully applied to solve various optimization problems in science and engineering. One such popular one is called global PSO (GPSO) algorithm. One of major drawback of GPSO algorithm is the phenomenon of “zigzagging”, that leads to premature convergence by falling into local minima. In addition, the performance of GPSO algorithm deteriorates for high-dimensional problems, especially in presence of nonlinear constraints. In this paper we propose a novel algorithm called, orthogonal PSO (OPSO) that alleviates the shortcomings of the GPSO algorithm. In OPSO algorithm, the m particles of the swarm are divided into two groups: active group and passive group. The d particles of the active group undergo an orthogonal diagonalization process and are updated in such way that their position vectors become orthogonally diagonalized. In the OPSO algorithm, the particles are updated using only one guide, thus avoiding the conflict between the two guides that occurs in the GPSO algorithm. We applied the OPSO algorithm for solving economic dispatch (ED) problem by taking three power systems under several power constraints imposed by thermal generating units (TGUs) and smart power grid (SPG), for example, ramp rate limits, and prohibited operating zones. In addition, the OPSO algorithm is also applied for ten selected shifted and rotated CEC 2015 benchmark functions. With extensive simulation studies, we have shown superior performance of OPSO algorithm over GPSO algorithm and several existing evolutional computation techniques in terms of several performance measures, e.g., minimum cost, convergence rate, consistency, and stability. In addition, using unpaired t-Test, we have shown the statistical significance of the OPSO algorithm against several contending algorithms including top-ranked CEC 2015 algorithms.

Optimal Yield Rate in ACF Cutting Process of TFT-LCD Module Using Orthogonal Particle Swarm Optimization Based on Response Surface Design

Anisotropic Conductive Film (ACF) is essential material in LCM (Liquid Crystal Module) process. It is used in bondingprocess to make the driving circuit conductive. Because the price of TFT-LCD is getting lower than before in recentyears, the ACF has relatively higher cost ratio. The conventional long bar ACF cutting unit is changed into short barACF cutting unit in new bonding technology. However, the new type machine was not optimized in process controland mechanical design. Therefore, the failure rate of new ACF cutting process is much higher than the one of theconventional process. This wastes the ACF material and rework cost is considerably large. How to make themanufacturing cost down effectively and promote the product quality is the main issue to maintain competitioncapability for the product. Therefore, the orthogonal particle swarm optimization (OPSO) is used to analyze theoptimal design problem. The ACF cutting yield rate is selected to be objective function for optimization. The qualitycharacteristic function for yield rate is used in orthogonal particle swarm optimization. Three control factors such asplasma clean speed, ACF peeling speed and ACF cutter spring setting are selected to study the effect of the yieldrate. Results show that the proposed method can provide good optimal solution to improve the ACF cutting processfor TFTLCD manufacturing process.

Optimal Yield Rate in ACF Cutting Process of TFT-LCD Module Using Orthogonal Particle Swarm Optimization

Anisotropic Conductive Film (ACF) is essential material in LCM (Liquid Crystal Module) process. It is used in bonding process to make the driving circuit conductive. Because the price of TFT-LCD is much lower than before in recent years, the ACF cost has relatively higher ratio in manufacture cost. The conventional long bar ACF cutting unit is changed to short bar ACF cutting unit in new bonding technology. However, the new type machine was not optimized in process and mechanical design. Therefore, the failure rate of new ACF cutting process is much higher than the one of the conventional process. This wastes the material and rework cost is considerably large. How to make the manufacturing cost down effectively and promote the product quality is the concern issue to maintain competition capability for the product. Therefore, the Orthogonal Particle Swarm Optimization is used to analyze the optimal design problem. The ACF cutting yield rate is selected to be objective function for optimization. The quality characteristic function is used in Orthogonal Particle Swarm Optimization. The plasma clean speed, ACF peeling speed and ACF cutter spring setting are selected to study the effect of the yield rate. Results show that the proposed method can provide good solution to improve the ACF cutting process for TFTLCD.

Orthogonal Particle Swarm Optimization Algorithm and Its Application in Circuit Design

In this paper, aim at the disadvantages of standard Particle Swarm Optimization (PSO) algorithm like being trapped easily into a local optimum, we improves the standard PSO and proposes a new algorithm to solve the overcomes of the standard PSO. The new algorithm keeps not only the fast convergence speed characteristic of PSO, but effectively improves the capability of global searching as well. Experiment results reveal that the proposed algorithm can find better solutions when compared to the standard particle swarm optimization algorithm. We use the proposed algorithm for digital circuit optimization design, and the final circuit is optimized in terms of complexity (with the minimum number of gates). DOI: http://dx.doi.org/10.11591/telkomnika.v11i6.2135

Robust trifocal tensor constraints for structure from motion estimation

It is important to estimate accurate camera parameters in multi-view stereo. In this paper, we use three-view relations, the trifocal tensor, to improve the Bundler, a popular structure from motion (SfM) system, for estimating accurate camera parameters. We propose a novel method: the Robust Orthogonal Particle Swarm Optimization (ROPSO) to estimate a robust and accurate trifocal tensor. In ROPSO, we formulate the trifocal tensor estimation as a global optimization problem and use the particle swarm optimization (PSO) for parameter searching. The orthogonal array is used to select the representative initial particles in PSO for more stable results. In the experiments, we use simulated and real ground truth data for statistical analysis. The experimental results show that the proposed ROPSO can achieve more accurate estimation of the trifocal tensor than the traditional methods and has higher probability to find the optimization solution than the traditional methods. Based on the trifocal tensor estimated by the proposed method, the SfM estimation errors can effectively be reduced. The average reprojection errors are reduced from 21.5 pixels to less than 1 pixel.

Dual Mechatronic MPPT Controllers With PN and OPSO Control Algorithms for the Rotatable Solar Panel in PHEV System

This paper aims at increasing the efficiency of the plug-in hybrid electric vehicle (PHEV) by using rotatable solar panel. Conventionally, the PHEV with solar panel has a critical problem of putting on the roof of a PHEV. Since the limited space on the roof of the vehicle is not large enough, rotatable structure is considered to track the sunlight by mechanical Petri-net (PN)-based maximum power point tracking (MPPT) control. A stepping motor is used to control the rotating angle of the rotating solar panel. In addition, the electric MPPT with orthogonal particle swarm optimization (OPSO) method is also included. With the dual mechatronic MPPT with PN+OPSO control algorithms, the maximum power in a limited roof space of the vehicle is possible. The solar panel has not to be very large. This will increase the efficiency of the PHEV. It is convinced that the proposed dual mechatronic PN+OPSO MPPT controllers are helpful to the PHEV system.

OPSO: Orthogonal Particle Swarm Optimization and Its Application to Task Assignment Problems

This paper proposes a novel variant of particle swarm optimization (PSO), named orthogonal PSO (OPSO), for solving intractable large parameter optimization problems. The standard version of PSO is associated with the lack of a mechanism responsible for the process of high-dimensional vector spaces. The high performance of OPSO arises mainly from a novel move behavior using an intelligent move mechanism (IMM) which applies orthogonal experimental design to adjust a velocity for each particle by using a systematic reasoning method instead of the conventional generate-and-go method. The IMM uses a divide-and- conquer approach to cope with the curse of dimensionality in determining the next move of particles. It is shown empirically that the OPSO performs well in solving parametric benchmark functions and a task assignment problem which is NP-complete compared with the standard PSO with the conventional move behavior. The OPSO with IMM is more specialized than the PSO and performs well on large-scale parameter optimization problems with few interactions between variables.