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Records with Keyword: Particle Swarm Optimization
Comparing Reinforcement Learning Methods for Real-Time Optimization of a Chemical Process
Titus Quah, Derek Machalek, Kody M. Powell
June 2, 2021 (v1)
Keywords: artificial neural networks, Particle Swarm Optimization, process optimization, Proximal Policy Optimization, real-time optimization, reinforcement learning
One popular method for optimizing systems, referred to as ANN-PSO, uses an artificial neural network (ANN) to approximate the system and an optimization method like particle swarm optimization (PSO) to select inputs. However, with reinforcement learning developments, it is important to compare ANN-PSO to newer algorithms, like Proximal Policy Optimization (PPO). To investigate ANN-PSO’s and PPO’s performance and applicability, we compare their methodologies, apply them on steady-state economic optimization of a chemical process, and compare their results to a conventional first principles modeling with nonlinear programming (FP-NLP). Our results show that ANN-PSO and PPO achieve profits nearly as high as FP-NLP, but PPO achieves slightly higher profits compared to ANN-PSO. We also find PPO has the fastest computational times, 10 and 10,000 times faster than FP-NLP and ANN-PSO, respectively. However, PPO requires more training data than ANN-PSO to converge to an optimal policy. This cas... [more]
DOA Estimation in Non-Uniform Noise Based on Subspace Maximum Likelihood Using MPSO
Jui-Chung Hung
May 25, 2021 (v1)
Keywords: direction of arrival estimation, memetic algorithms, non-uniform noise, Particle Swarm Optimization, subspace maximum-likelihood
In general, the performance of a direction of arrival (DOA) estimator may decay under a non-uniform noise and low signal-to-noise ratio (SNR) environment. In this paper, a memetic particle swarm optimization (MPSO) algorithm combined with a noise variance estimator is proposed, in order to address this issue. The MPSO incorporates re-estimation of the noise variance and iterated local search algorithms into the particle swarm optimization (PSO) algorithm, resulting in higher efficiency and a reduction in non-uniform noise effects under a low SNR. The MPSO procedure is as follows: PSO is initially utilized to evaluate the signal DOA using a subspace maximum-likelihood (SML) method. Next, the best position of the swarm to estimate the noise variance is determined and the iterated local search algorithm to reduce the non-uniform noise effect is built. The proposed method uses the SML criterion to rebuild the noise variance for the iterated local search algorithm, in order to reduce non-un... [more]
A Joint Optimization Strategy of Coverage Planning and Energy Scheduling for Wireless Rechargeable Sensor Networks
Cheng Gong, Chao Guo, Haitao Xu, Chengcheng Zhou, Xiaotao Yuan
April 30, 2021 (v1)
Keywords: coverage optimization, Particle Swarm Optimization, queuing game, virtual force, wireless rechargeable sensor network
Wireless Sensor Networks (WSNs) have the characteristics of large-scale deployment, flexible networking, and many applications. They are important parts of wireless communication networks. However, due to limited energy supply, the development of WSNs is greatly restricted. Wireless rechargeable sensor networks (WRSNs) transform the distributed energy around the environment into usable electricity through energy collection technology. In this work, a two-phase scheme is proposed to improve the energy management efficiency for WRSNs. In the first phase, we designed an annulus virtual force based particle swarm optimization (AVFPSO) algorithm for area coverage. It adopts the multi-parameter joint optimization method to improve the efficiency of the algorithm. In the second phase, a queuing game-based energy supply (QGES) algorithm was designed. It converts energy supply and consumption into network service. By solving the game equilibrium of the model, the optimal energy distribution str... [more]
Artificial Immune System in Doing 2-Satisfiability Based Reverse Analysis Method via a Radial Basis Function Neural Network
Shehab Abdulhabib Alzaeemi, Saratha Sathasivam
April 29, 2021 (v1)
Keywords: 2-satisfiability based reverse analysis, artificial bee colony, artificial immune system, differential evolution, Genetic Algorithm, Particle Swarm Optimization, radial basis functions neural network
A radial basis function neural network-based 2-satisfiability reverse analysis (RBFNN-2SATRA) primarily depends on adequately obtaining the linear optimal output weights, alongside the lowest iteration error. This study aims to investigate the effectiveness, as well as the capability of the artificial immune system (AIS) algorithm in RBFNN-2SATRA. Moreover, it aims to improve the output linearity to obtain the optimal output weights. In this paper, the artificial immune system (AIS) algorithm will be introduced and implemented to enhance the effectiveness of the connection weights throughout the RBFNN-2SATRA training. To prove that the introduced method functions efficiently, five well-established datasets were solved. Moreover, the use of AIS for the RBFNN-2SATRA training is compared with the genetic algorithm (GA), differential evolution (DE), particle swarm optimization (PSO), and artificial bee colony (ABC) algorithms. In terms of measurements and accuracy, the simulation results s... [more]
A Hybrid of Particle Swarm Optimization and Harmony Search to Estimate Kinetic Parameters in Arabidopsis thaliana
Mohamad Saufie Rosle, Mohd Saberi Mohamad, Yee Wen Choon, Zuwairie Ibrahim, Alfonso González-Briones, Pablo Chamoso, Juan Manuel Corchado
December 17, 2020 (v1)
Subject: Biosystems
Keywords: Arabidopsis thaliana, Harmony Search, parameter estimation, Particle Swarm Optimization
Recently, modelling and simulation have been used and applied to understand biological systems better. Therefore, the development of precise computational models of a biological system is essential. This model is a mathematical expression derived from a series of parameters of the system. The measurement of parameter values through experimentation is often expensive and time-consuming. However, if a simulation is used, the manipulation of computational parameters is easy, and thus the behaviour of a biological system model can be altered for a better understanding. The complexity and nonlinearity of a biological system make parameter estimation the most challenging task in modelling. Therefore, this paper proposes a hybrid of Particle Swarm Optimization (PSO) and Harmony Search (HS), also known as PSOHS, designated to determine the kinetic parameter values of essential amino acids, mainly aspartate metabolism, in Arabidopsis thaliana. Three performance measurements are used in this pap... [more]
Layout Optimization Process to Minimize the Cost of Energy of an Offshore Floating Hybrid Wind−Wave Farm
Jorge Izquierdo-Pérez, Bruno M. Brentan, Joaquín Izquierdo, Niels-Erik Clausen, Antonio Pegalajar-Jurado, Nis Ebsen
March 12, 2020 (v1)
Keywords: farm layout, floating offshore energy generation, hybrid wind-wave platform, LCOE, Optimization, Particle Swarm Optimization, PSO, sustainable energy generation
Offshore floating hybrid wind and wave energy is a young technology yet to be scaled up. A way to reduce the total costs of the energy production process in order to ensure competitiveness in the sustainable energy market is to maximize the farm’s efficiency. To do so, an energy generation and costs calculation model was developed with the objective of minimizing the technology’s Levelized Cost of Energy (LCOE) of the P80 hybrid wind-wave concept, designed by the company Floating Power Plant A/S. A Particle Swarm Optimization (PSO) algorithm was then implemented on top of other technical and decision-making processes, taking as decision variables the layout, the offshore substation position, and the export cable choice. The process was applied off the west coast of Ireland in a site of interest for the company, and after a quantitative and qualitative optimization process, a minimized LCOE was obtained. It was then found that lower costs of ~73% can be reached in the short-term, and th... [more]
Optimal Design of Standalone Photovoltaic System Based on Multi-Objective Particle Swarm Optimization: A Case Study of Malaysia
Hussein Mohammed Ridha, Chandima Gomes, Hashim Hizam, Masoud Ahmadipour
February 3, 2020 (v1)
Keywords: levelized cost of energy (LCE), life cycle cost (LCC), loss of load probability (LLP), multi-objective optimization, Particle Swarm Optimization, standalone PV system
This paper presents a multi-objective particle swarm optimization (MOPSO) method for optimal sizing of the standalone photovoltaic (SAPV) systems. Loss of load probability (LLP) analysis is considered to determine the technical evaluation of the system. Life cycle cost (LCC) and levelized cost of energy (LCE) are treated as the economic criteria. The two variants of the proposed PSO method, referred to as adaptive weights PSO ( A W P S O c f ) and sigmoid function PSO ( S F P S O c f ) , are implemented using MATLAB software to the optimize the number of PV modules in (series and parallel) and number of the storage battery. The case study of the proposed SAPV system is executed using the hourly meteorological data and typical load demand for one year in a rural area in Malaysia. The performance outcomes of the proposed A W / S F P S O c f methods give various configurations at desired levels of LLP values and the corresponding minimum cost. The perform... [more]
Review of Anaerobic Digestion Modeling and Optimization Using Nature-Inspired Techniques
Anjali Ramachandran, Rabee Rustum, Adebayo J. Adeloye
January 19, 2020 (v1)
Subject: Biosystems
Keywords: anaerobic digestion, ant colony optimization, artificial neural network, firefly algorithm, Genetic Algorithm, nature-inspired techniques, Particle Swarm Optimization
Although it is a well-researched topic, the complexity, time for process stabilization, and economic factors related to anaerobic digestion call for simulation of the process offline with the help of computer models. Nature-inspired techniques are a recently developed branch of artificial intelligence wherein knowledge is transferred from natural systems to engineered systems. For soft computing applications, nature-inspired techniques have several advantages, including scope for parallel computing, dynamic behavior, and self-organization. This paper presents a comprehensive review of such techniques and their application in anaerobic digestion modeling. We compiled and synthetized the literature on the applications of nature-inspired techniques applied to anaerobic digestion. These techniques provide a balance between diversity and speed of arrival at the optimal solution, which has stimulated their use in anaerobic digestion modeling.
The State of Art in Particle Swarm Optimization Based Unit Commitment: A Review
Gad Shaari, Neyre Tekbiyik-Ersoy, Mustafa Dagbasi
December 10, 2019 (v1)
Keywords: Particle Swarm Optimization, solar, thermal, unit commitment, Wind
Unit Commitment (UC) requires the optimization of the operation of generation units with varying loads, at every hour, under different technical and environmental constraints. Many solution techniques were developed for the UC problem, and the researchers are still working on improving the efficiency of these techniques. Particle swarm optimization (PSO) is an effective and efficient technique used for solving the UC problems, and it has gotten a considerable amount of attention in recent years. This study provides a state-of-the-art literature review on UC studies utilizing PSO or PSO-variant algorithms, by focusing on research articles published in the last decade. In this study, these algorithms/methods, objectives, constraints are reviewed, with focus on the UC problems that include at least one of the wind and solar technologies, along with thermal unit(s). Although, conventional PSO is one of the most effective techniques used in solving UC problem, other methods were also develo... [more]
Finding better limit cycles of semicontinuous distillation. Part 1: Back-stepping design Methodology
Pranav Bhaswanth Madabhushi, Thomas Adams II
July 9, 2019 (v1)
Keywords: Hybrid dynamic system, Limit Cycle, Particle Swarm Optimization, Process Design, Semicontinuous Distillation
Semicontinuous ternary zeotropic distillation is a periodic process that is carried out
in a single distillation column and a tightly integrated external middle vessel. In the
state-of-the-art design procedure of this process, a continuous distillation process that
separates the top and bottoms products to the desired purity is used to generate an arbitrary
initial state for simulating the dynamics of the semicontinuous distillation process.
Although this method is useful in estimating the limit cycle, it was later found that the
operation of the process in this limit cycle was economically sub-optimal. In this study,
a new algorithmic design procedure, called the back-stepping design methodology, is
proposed to find better limit cycles for zeotropic ternary semicontinuous distillation
using the aspenONE Engineering suite. The proposed methodology was applied to two
different case studies using feed mixtures with different chemical components. A comparison
with the current d... [more]
Finding better limit cycles of semicontinuous distillation
Pranav Bhaswanth Madabhushi, Thomas Adams II
March 22, 2019 (v1)
There are three different ways of operating the distillation process based on production requirements and operational flexibility. Semicontinuous distillation of multicomponent mixtures is a cost-effective technology in the intermediate production range when compared with traditional batch and continuous distillation processes. The process, which has both continuous and discrete dynamics, operates in a limit cycle (an isolated periodic orbit). Design of this process entails finding the system’s time-invariant parameters, for example, equipment design parameters, reflux rate etc., to operate in a limit cycle having acceptable performance. In semicontinuous distillation studies, the performance metric chosen is the separation cost, which is defined as the total annualized cost-per-production. The state-of-the-art design procedure involves determining an initial state for estimating the limit cycle through the dynamic simulation of the process and is found to be effective. However, it lac... [more]
Short-Term Load Forecasting Using Adaptive Annealing Learning Algorithm Based Reinforcement Neural Network
Cheng-Ming Lee, Chia-Nan Ko
February 27, 2019 (v1)
Keywords: adaptive annealing learning algorithm, Particle Swarm Optimization, radial basis function neural network, short-term load forecasting, support vector regression
A reinforcement learning algorithm is proposed to improve the accuracy of short-term load forecasting (STLF) in this article. The proposed model integrates radial basis function neural network (RBFNN), support vector regression (SVR), and adaptive annealing learning algorithm (AALA). In the proposed methodology, firstly, the initial structure of RBFNN is determined by using an SVR. Then, an AALA with time-varying learning rates is used to optimize the initial parameters of SVR-RBFNN (AALA-SVR-RBFNN). In order to overcome the stagnation for searching optimal RBFNN, a particle swarm optimization (PSO) is applied to simultaneously find promising learning rates in AALA. Finally, the short-term load demands are predicted by using the optimal RBFNN. The performance of the proposed methodology is verified on the actual load dataset from the Taiwan Power Company (TPC). Simulation results reveal that the proposed AALA-SVR-RBFNN can achieve a better load forecasting precision compared to various... [more]
A Current Control Approach for an Abnormal Grid Supplied Ultra Sparse Z-Source Matrix Converter with a Particle Swarm Optimization Proportional-Integral Induction Motor Drive Controller
Seyed Sina Sebtahmadi, Hanieh Borhan Azad, Didarul Islam, Mehdi Seyedmahmoudian, Ben Horan, Saad Mekhilef
January 31, 2019 (v1)
Keywords: induction motor drives, matrix converter, Particle Swarm Optimization, Z-source network
A rotational d-q current control scheme based on a Particle Swarm Optimization- Proportional-Integral (PSO-PI) controller, is used to drive an induction motor (IM) through an Ultra Sparse Z-source Matrix Converter (USZSMC). To minimize the overall size of the system, the lowest feasible values of Z-source elements are calculated by considering the both timing and aspects of the circuit. A meta-heuristic method is integrated to the control system in order to find optimal coefficient values in a single multimodal problem. Henceforth, the effect of all coefficients in minimizing the total harmonic distortion (THD) and balancing the stator current are considered simultaneously. Through changing the reference point of magnitude or frequency, the modulation index can be automatically adjusted and respond to changes without heavy computational cost. The focus of this research is on a reliable and lightweight system with low computational resources. The proposed scheme is validated through bot... [more]
Multi-Objective Sustainable Operation of the Three Gorges Cascaded Hydropower System Using Multi-Swarm Comprehensive Learning Particle Swarm Optimization
Xiang Yu, Hui Sun, Hui Wang, Zuhan Liu, Jia Zhao, Tianhui Zhou, Hui Qin
November 28, 2018 (v1)
Subject: Optimization
Keywords: comprehensive learning, hydropower reservoir system, multi-objective optimal operation, multi-swarm, Particle Swarm Optimization
Optimal operation of hydropower reservoir systems often needs to optimize multiple conflicting objectives simultaneously. The conflicting objectives result in a Pareto front, which is a set of non-dominated solutions. Non-dominated solutions cannot outperform each other on all the objectives. An optimization framework based on the multi-swarm comprehensive learning particle swarm optimization algorithm is proposed to solve the multi-objective operation of hydropower reservoir systems. Through adopting search techniques such as decomposition, mutation and differential evolution, the algorithm tries to derive multiple non-dominated solutions reasonably distributed over the true Pareto front in one single run, thereby facilitating determining the final tradeoff. The long-term sustainable planning of the Three Gorges cascaded hydropower system consisting of the Three Gorges Dam and Gezhouba Dam located on the Yangtze River in China is studied. Two conflicting objectives, i.e., maximizing h... [more]
Hybridizing DEMD and Quantum PSO with SVR in Electric Load Forecasting
Li-Ling Peng, Guo-Feng Fan, Min-Liang Huang, Wei-Chiang Hong
November 27, 2018 (v1)
Keywords: auto regression, differential empirical mode decomposition, electric load forecasting, Particle Swarm Optimization, quantum theory, support vector regression
Electric load forecasting is an important issue for a power utility, associated with the management of daily operations such as energy transfer scheduling, unit commitment, and load dispatch. Inspired by strong non-linear learning capability of support vector regression (SVR), this paper presents an SVR model hybridized with the differential empirical mode decomposition (DEMD) method and quantum particle swarm optimization algorithm (QPSO) for electric load forecasting. The DEMD method is employed to decompose the electric load to several detail parts associated with high frequencies (intrinsic mode function—IMF) and an approximate part associated with low frequencies. Hybridized with quantum theory to enhance particle searching performance, the so-called QPSO is used to optimize the parameters of SVR. The electric load data of the New South Wales (Sydney, Australia) market and the New York Independent System Operator (NYISO, New York, USA) are used for comparing the forecasting perfor... [more]
A Robust Weighted Combination Forecasting Method Based on Forecast Model Filtering and Adaptive Variable Weight Determination
Lianhui Li, Chunyang Mu, Shaohu Ding, Zheng Wang, Runyang Mo, Yongfeng Song
October 23, 2018 (v1)
Keywords: combination forecast, immune algorithm, load forecasting, Markov chain, normal cloud model, Particle Swarm Optimization, robustness
Medium-and-long-term load forecasting plays an important role in energy policy implementation and electric department investment decision. Aiming to improve the robustness and accuracy of annual electric load forecasting, a robust weighted combination load forecasting method based on forecast model filtering and adaptive variable weight determination is proposed. Similar years of selection is carried out based on the similarity between the history year and the forecast year. The forecast models are filtered to select the better ones according to their comprehensive validity degrees. To determine the adaptive variable weight of the selected forecast models, the disturbance variable is introduced into Immune Algorithm-Particle Swarm Optimization (IA-PSO) and the adaptive adjustable strategy of particle search speed is established. Based on the forecast model weight determined by improved IA-PSO, the weighted combination forecast of annual electric load is obtained. The given case study i... [more]
Reactive Power Dispatch Optimization with Voltage Profile Improvement Using an Efficient Hybrid Algorithm †
Zahir Sahli, Abdellatif Hamouda, Abdelghani Bekrar, Damien Trentesaux
September 21, 2018 (v1)
Keywords: hybrid method, loss minimization, optimal reactive power dispatch, Particle Swarm Optimization, tabu search, voltage deviation
This paper presents an efficient approach for solving the optimal reactive power dispatch problem. It is a non-linear constrained optimization problem where two distinct objective functions are considered. The proposed approach is based on the hybridization of the particle swarm optimization method and the tabu-search technique. This hybrid approach is used to find control variable settings (i.e., generation bus voltages, transformer taps and shunt capacitor sizes) which minimize transmission active power losses and load bus voltage deviations. To validate the proposed hybrid method, the IEEE 30-bus system is considered for 12 and 19 control variables. The obtained results are compared with those obtained by particle swarm optimization and a tabu-search without hybridization and with other evolutionary algorithms reported in the literature.
Joint Operation between a PSO-Based Global MPP Tracker and a PV Module Array Configuration Strategy under Shaded or Malfunctioning Conditions
Pi-Yun Chen, Kuei-Hsiang Chao, Bo-Jyun Liao
September 21, 2018 (v1)
Keywords: configuration strategy, maximum power point tracker, Particle Swarm Optimization, photovoltaic module array, shaded or malfunctioning
This paper aims to present a smart, particle swarm optimization (PSO)-based, real time configuration strategy for a photovoltaic (PV) module array in the event of shadow cast on a PV module(s) and/or module failure as an effective approach to power generation efficiency elevation. At the first step, the respective maximum output power levels provided by a normal operating array at various levels of irradiation and module surface temperatures are measured and entered as references into a database. Subsequently, the maximum output power (MPP) level, tracked by a MPP tracker, is feedbacked for a comparison with an aforementioned reference as a way to tell whether there is either a shadow or a malfunction event on a PV module(s). Once an abnormal operation is detected, the presented smart configuration algorithm is performed to reconfigure the PV module array such that the array is operated at the global MPP as intended. Furthermore, by use of a PIC microcontroller that is a family of micr... [more]
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