particle swarm optimization research papers

According to the results, a DG is installed considering the best place and the best capacity for minimum losses at bus 24 with a capacity of 1.6115MW. Trends Comput. This development was done by controlling the inertia weight that affected the updating velocity of particles in the algorithm. M. B. Ghalia, Particle swarm optimization with an improved exploration-exploitation balance, in Proceedings of the 2008 51st Midwest Symposium on Circuits and Systems, Knoxville, TN, USA, August 2008. A Hybrid Particle Swarm Optimization with Dragonfly for Adaptive ANFIS to Model the Corrosion Rate in Concrete Structures Full-text available Dec 2022 Gholam Reza Khayati Zahra Rajabi Maryam. This means that the average elapsed time is reduced by 21% by using the proposed PSO algorithm as compared to the conventional PSO algorithm that is based on the liner inertia weight equation. The papers have been categorized depending upon the aspects of PSO explored as follows: 1. Review Papers 2. Taking into account the improvement ideas proposed by scholars in the early stage and the shortcomings still existing in the improvement, this paper puts forward the idea of improving particle swarm optimization algorithm in the future. Then, we analyze its present situation of research and application in algorithm structure, parameter selection, topology structure, discrete PSO algorithm and parallel PSO algorithm, multi-objective optimization PSO and its engineering . Eng. 185, pp. Following that, the applied improved PSO algorithm successfully found the optimal size and location of the desired DG unit with a capacity of 1.6722MW at bus number 10. The inspiration to introduce BPSO gets from the way that, in rivalry with other meta-heuristics, BPSO has demonstrated to be a champ by and large, putting a technique as a genuine alternative when one needs to take care of a complex optimization problem. 140, pp. rithm. Liu, S.C. Chaim, A competitive and cooperative co-evolutionary approach to multi-objective particle swarm optimization algorithm design. Proceedings of the Congress on Evolutionary Computation 1999, p. 1958-1962: By P. N. Suganthan: Training Product Units in Feedforward Neural Networks using Particle Swarm Optimization. Right here, we have countless book particle swarm optimization research toolbox documentation and collections to check out. Particle Swarm Optimiser with Neighbourhood Operator. This paper presents an overview of the research progress in Particle Swarm Optimization (PSO) during 19952017. Each particle has its own dimensional coordinate position and the particles perform the swarm. Gounaris, A review of recent advances in global optimization. 5, no. As a result, the proposed PSO performs better than the conventional PSO algorithm. Support Vector Machine (SVM) Adewumi, An Adaptive Velocity Particle Swarm Optimization for High-Dimensional Function Optimization Congress on Evolutionary Computation, June 2023, Cancn, Mxico (IEEE, 2013), L. Baiqum, G. Gaiquin, L. Zeyu, The block diagram method for designing the particle swarm optimization. The buses which are in the range of bus 19 to bus 27 have the lowest voltage magnitude as compared to other buses. 56, 12471293 (2013). Several aspects must be taken into consideration in this optimization process such as the number, capacity, and location of the DG units [2]. 67, pp. To learn more, view ourPrivacy Policy. J. Comput. Autom. Considering the nature of the installed DG system (generates real power only), the savings of the reactive power are neglected. By using our site, you agree to our collection of information through the use of cookies. In the meanwhile, GSA virtually squeezes the problem search space so as to maintain adequate diversity. 52(689), 710 (2012), Electrical Engineering Department, Delhi Technological University, Delhi, 110042, India, You can also search for this author in In the meanwhile, nonlinear strategies not only suggest in the initial stage in a better way, but they also reduce the time needed to get the optimum solution. Particle Swarm Optimization is useful to optimize the best variables combination for automating the features selection in machine learning models. oil rubbed bronze schluter strip programs for high school dropouts near me lochinvar knight whn085 manual lspdfr 2020 explorer els night out in okc huawei battery 5kw . 4(3), 167190 (2016), Article several color-cover sublines have been added featuring, beyond a collection of papers . Jyoti Jain. The fast growth of electrical power demand makes the transmission systems reach their maximum capacity. Comput. Part B Cybern. Appl. The total generation of the network is 5.89MW () and 3.52MVAr (). Power Energy Syst. 9, pp. It is a searching approach that benefits from particles with their fitness factors. In this paper, a two stage PSO algorithm is presented to solve the optimal long-term operation of a hydroelectric power system. F. Abu-Mouti and M. El-Hawary, Optimal distributed generation allocation and sizing in distribution systems via artificial bee colony algorithm, IEEE Transactions on Power Delivery, vol. 16, no. Proceedings. 7(1), 179186 (2013), M.A. 20902101, 2011. Bonyadi, Z. Michalewicz, Impacts of coefficients on movement patterns in the particle swarm optimization algorithm. The LNCS series reports state-of-the-art results in computer science research, development, and education, at a high level and in both printed and electronic . 317325, 2013. Content from this work may be used under the terms of the Creative Commons Attribution 3.0 licence. In this research, the cost of the kWh is assumed to be 0.14 USD. By comparing the magnitude of change of the R squared values before and after the use of PSO feature selection, the result showed that the automated features selection has improved the results of all the machine learning algorithms mainly in the linear-based machine learning (Linear Regression, Lasso, Ridge). In this paper, an improved particle swarm optimization method (PSO) is proposed to optimally size and place a DG unit in an electrical power system so as to improve voltage profile and reduce active power losses in the system. Optim. Provided by the Springer Nature SharedIt content-sharing initiative, Over 10 million scientific documents at your fingertips, Not logged in J. Kim, S. Nam, S. Park, and C. Singh, Dispersed generation planning using improved Hereford ranch algorithm, Electric Power Systems Research, vol. J. Comput. The research papers concerning PSO have been considered from 1995 to 2017. While a bigger impact has been given by the Airbnb community to the local economic development of each country, there has been very little effort that investigates on Airbnb pricing issue with machine learning techniques. This paper introduces its origin and background and carries out the theory analysis of the PSO. Inf. IEEE Trans. The purpose of this paper is to show how the search algorithm known as particle swarm optimization performs. Using these two concepts, the particles in a swarm try to find the optimum solution with the help of their memory by storing data such as their own best position and the global best position for the whole swarm [23]. Nguyen, A self-learning particle swarm optimizer for global optimization problems. Tang, L.-X. J. Kennedy and R. C. Eberhart, Particle swarm optimization, in Proceedings of the IEEE International Conference on Neural Networks, pp. PSO is a Simulation of a simplified social system. The particle swarm optimization concept and paradigm presented in this paper seem to adhere to all five principles. Early detection of this disease is the most efficient way to prevent its rapid spread. 214, 3035 (2008), S. Hossen, F. Rabbi, M. Rahman, Adaptive particle swarm optimization (APSO) for multimodal function optimization. In the meanwhile, in [19], a new algorithm called PFDE is proposed for multiple DG sizing and location. Math. Sorry, preview is currently unavailable. The population is responding to the quality factors pbest and gbest. Comput. Abido, Optimal power flow using particle swarm optimization. Res. Binary particle swarm optimization (BPSO) method was utilized to take care of optimal power flow issue with different targets under system contingencies. Particle Swarm Optimization (PSO) algorithm has become more popular recently. 196207, 2012. 1, pp. In 1995, Kennedy and Eberhart wrote a research paper based on the social behavior of animal groups, where they had stated that sharing information among the group increases survival advantage. Citation Shouya Wang et al 2021 J., M. Chen, T. Wang, J. Feng, Y.-Y. The positioning is assessed utilizing fuzzy logic. It is compared with the linearly decreasing weight particle swarm algorithm, and the test is performed on four typical single-peak and multi-peak functions. 512, 2002. In this chapter, a particle swarm optimization (PSO) algorithm has been proposed for optimal placement and sizing of DG to improve voltage stability index in the radial distribution system. Evolut. Harald Yndestad, Filippo Sanfilippo, Yndestad Harald, International Journal on Computational Science & Applications (IJCSA), International Transactions on Electrical Energy Systems, 2007 IEEE International Conference on Industrial Engineering and Engineering Management, Two stage particle swarm optimisation for long-term operation of a hydroelectric power system, A particle swarm optimization for reactive power and voltage control considering voltage security assessment, Optimization techniques for business process analysis on automotive industry in Malaysia, Symbol Detection Techniques in a Spatial Multiplexing System, A particle swarm optimization for reactive power and voltage control considering voltage stability, Study and Analysis of Particle Swarm Optimization: A Review, Swarm intelligence based inverse model for characterization of groundwater contaminant source, Search optimization using hybrid particle sub-swarms and evolutionary algorithms, Creativity and Autonomy in Swarm Intelligence Systems, Cooperation of Nature and Physiologically Inspired Mechanism in Visualisation, Design of Optimal Linear Phase FIR High Pass Filter using Craziness based Particle Swarm Optimization Technique, Particle Swarm Optimization with Constriction Factor and Inertia Weight Approach Based Synthesis of Concentric Circular Antenna Array with Non-isotropic Elements, Feature selection based on rough sets and particle swarm optimization, Performance of GA and PSO aided SDMA/OFDM Over-Loaded System in a Near-Realistic Fading Environment, Optimal linear phase fir high pass filter design using PSOCFIWA-WM, UNIT COMMITMENT OF THERMAL GENERATING UNITS USING ADVANCED PARTICLE SWARM OPTIMIZATION TECHNIQUES, Swarm Intelligence based Soft Computing Techniques for the Solutions to Multiobjective Optimization Problems, A quantum particle swarm optimizer with chaotic mutation operator, Particle Swarm Optimization - The Fundamental Insight, High speed end-milling optimisation using Particle Swarm Intelligence, Computational algorithms inspired by biological processes and evolution, Optimal linear phase fir high pass filter design using PSOCFIWA-WM (IEEE), Chaotic Particle Swarm Optimization for Congestion Management in an Electricity Market, Software Project Scheduling Management by Particle Swarm Optimization, Contingency Constrained Optimal Power Flow by Hybrid Optimization Technique Using FACTS Device, Multi-Objective VAR Dispatch Using Particle Swarm Optimization, A software tool for teaching of particle swarm optimization fundamentals, Application of Particle Swarm Optimization Technique in Hexagonal and Concentric Hexagonal Antenna Array for Side Lobe Level Reduction, Optimisation of Boids Swarm Model Based on Genetic Algorithm and Particle Swarm Optimisation Algorithm (Comparative Study), A NEW APPROACH TO THE SOLUTION OF ECONOMIC DISPATCH USING PARTICLE SWARM OPTIMIZATION WITH SIMULATED ANNEALING, Social and Business Intelligence Analysis Using PSO, Business and Social Behaviour Intelligence Analysis Using PSO, (2006) Particle Swarm Optimization: Development of a General-Purpose Optimizer, (2006) Population-Based Methods: Particle Swarm Optimization. El-Hawary, A survey of particle swarm optimization applications in electric power systems. B. Singh and J. Sharma, A review on distributed generation planning, Renewable and Sustainable Energy Reviews, vol. 25, pp. Experimental results show that the proposed algorithm can achieve competitive performance with fewer features, which is suitable for edge computing devices with lower computation power. 529544, 2017. PSCE 09. 17, Z.L. Binary Particle Swarm Optimization Optimization Approach Power System Security Swarm Optimization Improvement of power system security manages the errand of making healing move against conceivable system overloads in the framework following the events of contingencies. 293312, 2017. The unit for Ploss is kW and the normalization equation is as follows: The mathematical equation of objective function for DG sizing is formulated aswhere , where Ii and Ri are the electric current and electrical resistance at bus i, respectively. In this paper, an improved particle swarm optimization method (PSO) is proposed to optimally size and place a DG unit in an electrical power system so as to improve voltage profile and reduce active power losses in the system. |a Abstract This paper presents an overview of the research progress in Particle Swarm Optimization (PSO) during 1995-2017. As a result, the PSO algorithm reduced power losses and improved the systems voltage profile. 6, pp. 633640, 978-1-4577-1124-4/11/, M.R. H. Hamedi and M. Gandomkar, A straightforward approach to minimizing unsupplied energy and power loss through DG placement and evaluating power quality in relation to load variations over time, International Journal of Electrical Power & Energy Systems, vol. In this case, the cost of the suggested DG is 1,933,800 USD while the amount of power losses is reduced by 0.104MW. Eng. 245248. 13(4), 913918 (2016), N.K. Generation re-dispatching is answer for the evacuation of line overloads. The proposed objective function aimed to minimize the real power losses, voltage deviation, average voltage total harmonic distortion (THDv), and system average voltage dip magnitude (SAVDM). Particle Swarm Optimization (hereafter, PSO) as its name suggests is an optimization . In order to show the significance of the proposed algorithm, a comparison is conducted with the conventional PSO algorithm for the same case. M. Obaidy and A. Ayesh, The implementation of optimization algorithm for energy efficient dynamic ad hoc wireless sensor networks, in Proceedings of the 4th International Workshop on Advance Computation for Engineering Applications, pp. 1 of 20 Particle swarm optimization Nov. 09, 2013 12 likes 4,023 views Download Now Download to read offline Education Technology Particle swarm optimization Ahmed Fouad Ali Follow Professor at Faculty of Computers and Information, Ismailia, Egypt. Soft Comput. Suganthan, S. Baskar, Comprehensive learning particle swarm optimizer for global optimization of multimodal functions. , "The problem of determining the optimal long-term operation of a hydroelectric power system has been the subject of numerous publications over the past sixty years. You can download the paper by clicking the button above. Results showed that 5.58% of the total active power generation was consumed as active power losses before the installation of the DG unit in the electrical power system. We additionally have the funds for variant types and afterward type of the books to browse. 56(7), 19391947 (2008), H. Duan, P. Li, Y. Yu, A predator-prey Particle swarm optimization approach to multiple UCAV air combat modeled by dynamic game theory. The Search starts with the random initialization of a population. The new velocity and position are calculated using equations (, Finally, the optimum value or the desired output is set as, Applied Computational Intelligence and Soft Computing. This is a preview of subscription content, access via your institution. Abstract: Particle swarm optimization (PSO) explores global optimal solution through exploiting the particle's memory and the swarm's memory. In the used PSO code, 50 particles are generated to find the size and location of one DG unit which has three dimensions. 70, pp. 9396, 2012. Vrahatis, Recent approaches to global optimization problems through particle swarm optimization. Mag. Abstract. Google Scholar, K. Kameyama, Particle swarm optimizationa survey. Abstract- This paper surveys the research and development of Particle Swarm Optimization (PSO) algorithm for choosing the suitable position and size of Distributed Generation (DG) units within a distribution network. Particle Swarm Optimization (PSO), which was proposed by Kennedy J. and Eberhart R.C [35] is within the category of heuristic optimization methods that follow the cooperation and intelligence issues of swarms in the nature. Google Scholar, Q. Wu, C. Cole, T. McSweeng, Applications of particle swarm optimization in the railway domain. 75, pp. In the meanwhile, the introduction of DG to the electrical power system changes the nature of the system from passive networks to an active network. J. In addition, PSO has three main dominant constants which are inertia weight , c1, and c2 whereas any change in these three constants will affect the performance of the algorithm. A. M. El-Zonkoly, Optimal placement of multi-distributed generation units including different load models using particle swarm optimisation, IET Generation, Transmission & Distribution, vol. DG units are grid-connected units that are located near customers and deliver power to the grid regardless of its capacity or type. Simulation Results on IEEE-14 and IEEE-30 bus systems are presented with different objectives. In PSO population (swarm) are encoded as particles. IJSRD - International Journal for Scientific Research and Development, International Journal of Emerging Electric Power Systems, Advances in Intelligent Systems and Computing. In this research, the optimal sizing of DG is done considering a conventional PSO algorithm with a liner inertia weight equation. 1927, 2011. Shamsuddin, S.S. Yuhaniz, Binary accelerated particle swarm algorithm (BAPSA) for discrete optimization problems. (2017). The minimum value of fitness losses using the applied algorithm is found to be 0.0.0406 while the average elapsed time is 62.2325s. In addition to that, the proposed PSO algorithm reduces the active power losses by 31.6%. The DG is limited by the available power sources at any given location. This technique has attracted many researchers because of its simplicity which led to many improvements and modifications of the basic PSO. 1723, UK, 2008. Active and reactive power losses for the system before the DG installation. Particle Swarm Optimization And Intelligence Advances And Applications Premier Reference . Abstract: Particle swarm optimization (PSO) is one of the most well-regarded swarm-based algorithms in the literature. Enter the email address you signed up with and we'll email you a reset link. IEEE Trans. Key Words: particle swarm optimization; long-tem operation; hydroelectric power system; large-scale optimization", on Intelligent System Applications to Power , Handbook of Nature-Inspired and Innovative . The adopted electrical network consists of 34 buses with one generation bus, 29 load buses, and 33 branches. IEEE/PES, 1518 March 2009, pp. This paper highlights the basic background needed to understand and implement the PSO algorithm. 38, no. D. B. Prakash and C. Lakshminarayana, Multiple dg placements in distribution system for power loss reduction using pso algorithm, Procedia Technology, vol. In addition, constants are used to update the velocity of the algorithm such as inertia weight which has 0.9 and 0.4 as maximum and minimum values, respectively, as recommended by [30]. According to [34], the total cost of 1kWp of a large-scale distributed generation is about 1,200USD/Wp. The relationships between particle swarm optimization and both artificial life and genetic algorithms are described. In this paper, a detection algorithm is developed, which can detect COVID-19 effectively by utilizing the features from Schutteand, A.A. Groenwold, A study of global optimization using particle swarms. Y. Shi and R. Eberhart, Fuzzy adaptive particle swarm optimization, in Proceedings of the 2001 Congress on Evolutionary Computation, pp., Z. Chen, Y. Bo, P. Wu, W. Zhou, A new particle filter based on organizational adjustment particle swarm optimization. 59, 182196 (2017), A. Suresha, K.V. MathSciNet IEEE 34 bus network is used as a case study to perform this research. This paper analyzes a particle's trajectory as it moves in discrete time (the algebraic view), then progresses to the view of it in continuous time (the analytical view). (iv)The new velocity and position are calculated using equations (1) and (2) for the next iteration. Rios, N.V. Sahinidis, Derivative-free optimization: a review of algorithms and comparison of software implementations.
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