Relevant bibliographies by topics / PSO (Particle Swarm Optimization) and Loss reduction

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  1. Journal articles
  2. Dissertations / Theses
  3. Book chapters
  4. Conference papers

Academic literature on the topic 'PSO (Particle Swarm Optimization) and Loss reduction'

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Published: 3 June 2025
Last updated: 31 July 2025

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Journal articles on the topic "PSO (Particle Swarm Optimization) and Loss reduction"

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Lenin, K. "REDUCTION OF ACTIVE POWER LOSS BY VOLITION PARTICLE SWARM OPTIMIZATION." International Journal of Research -GRANTHAALAYAH 6, no. 6 (2018): 346–56. http://dx.doi.org/10.29121/granthaalayah.v6.i6.2018.1379.

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This paper projects Volition Particle Swarm Optimization (VP) algorithm for solving optimal reactive power problem. Particle Swarm Optimization algorithm (PSO) has been hybridized with the Fish School Search (FSS) algorithm to improve the capability of the algorithm. FSS presents an operator, called as collective volition operator, which is capable to auto-regulate the exploration-exploitation trade-off during the algorithm execution. Since the PSO algorithm converges faster than FSS but cannot auto-adapt the granularity of the search, we believe the FSS volition operator can be applied to the
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Dr.K.Lenin. "REDUCTION OF ACTIVE POWER LOSS BY VOLITION PARTICLE SWARM OPTIMIZATION." International Journal of Research - Granthaalayah 6, no. 6 (2018): 346–56. https://doi.org/10.5281/zenodo.1308981.

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This paper projects Volition Particle Swarm Optimization (VP) algorithm for solving optimal reactive power problem. Particle Swarm Optimization algorithm (PSO) has been hybridized with the Fish School Search (FSS) algorithm to improve the capability of the algorithm. FSS presents an operator, called as collective volition operator, which is capable to auto-regulate the exploration-exploitation trade-off during the algorithm execution. Since the PSO algorithm converges faster than FSS but cannot auto-adapt the granularity of the search, we believe the FSS volition operator can be applied to the
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Dr.K.Lenin. "ACTIVE POWER LOSS REDUCTION BY BETTER-QUALITY PARTICLE SWARM OPTIMIZATION ALGORITHM." International Journal of Research - Granthaalayah 6, no. 1 (2018): 329–37. https://doi.org/10.5281/zenodo.1167554.

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In this paper Better-Quality Particle Swarm Optimization (BPSO) algorithm is proposed to solve the optimal reactive power Problem. Proposed algorithm is obtained by combining particle swarm optimization (PSO), Cauchy mutation and an evolutionary selection strategy. The idea is to introduce the Cauchy mutation into PSO in the hope of preventing PSO from trapping into a local optimum through long jumps made by the Cauchy mutation. In order to evaluate the efficiency of the proposed Better-Quality Particle Swarm Optimization (BPSO) algorithm, it has been tested on IEEE 57 bus system. Simulation R
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Lenin, K. "ACTIVE POWER LOSS REDUCTION BY BETTER-QUALITY PARTICLE SWARM OPTIMIZATION ALGORITHM." International Journal of Research -GRANTHAALAYAH 6, no. 1 (2018): 329–37. http://dx.doi.org/10.29121/granthaalayah.v6.i1.2018.1626.

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In this paper Better-Quality Particle Swarm Optimization (BPSO) algorithm is proposed to solve the optimal reactive power Problem. Proposed algorithm is obtained by combining particle swarm optimization (PSO), Cauchy mutation and an evolutionary selection strategy. The idea is to introduce the Cauchy mutation into PSO in the hope of preventing PSO from trapping into a local optimum through long jumps made by the Cauchy mutation. In order to evaluate the efficiency of the proposed Better-Quality Particle Swarm Optimization (BPSO) algorithm, it has been tested on IEEE 57 bus system. Simulation R
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Eshan, Karunarathne, Pasupuleti Jagadeesh, Ekanayake Janaka, and Almeida Dilini. "Comprehensive learning particle swarm optimization for sizing and placement of distributed generation for network loss reduction." Indonesian Journal of Electrical Engineering and Computer Science 20, no. 1 (2020): 16–23. https://doi.org/10.11591/ijeecs.v20.i1.pp16-23.

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With the technological advancements, distributed generation (DG) has become a common method of overwhelming the issues like power losses and voltage drops which accompanies with the leaf of the feeders of radial distribution networks. Many researchers have used several optimization techniques and tools which could be used to locate and size the DG units in the system. Particle swarm optimization (PSO) is one of the famous optimization techniques. However, the premature convergence is identified as a fundamental adverse effect of this optimization technique. Therefore, the optimization problem
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Lenin, Kanagabasai. "Factual power loss reduction by dynamic membrane evolutionary algorithm." International Journal of Advances in Applied Sciences (IJAAS) 10, no. 2 (2021): 99–106. https://doi.org/10.11591/ijaas.v10.i2.pp99-106.

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This paper presents Dynamic Membrane Evolutionary Algorithm (DMEA) has been applied to solve optimal reactive power problem. Proposed methodology merges the fusion and division rules of P systems with active membranes and with adaptive differential evolution (ADE), particle swarm optimization (PSO) exploration stratagem. All elementary membranes are amalgamated into one membrane in the computing procedure. Furthermore, integrated membrane are alienated into the elementary membranes 1, 2,_ m. In particle swarm optimization (PSO) 𝑪<sub>𝟏</sub>, 𝑪<sub>𝟐</sub> (acceleration constants) are vital pa
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Dr, K. Lenin. "HYBRIDIZATION OF ANT COLONY ALGORITHM AND PARTICLE SWARM OPTIMIZATION ALGORITHM FOR REDUCTION OF REAL POWER LOSS." International Journal of Research - Granthaalayah 6, no. 12 (2018): 121–27. https://doi.org/10.5281/zenodo.2532382.

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In this work Ant colony optimization algorithm (ACO) &amp; particle swarm optimization (PSO) algorithm has been hybridized (called as APA) to solve the optimal reactive power problem. In this algorithm, initial optimization is achieved by particle swarm optimization algorithm and then the optimization process is carry out by ACO around the best solution found by PSO to finely explore the design space. In order to evaluate the proposed APA, it has been tested on IEEE 300 bus system and compared to other standard algorithms. Simulations results show that proposed APA algorithm performs well in r
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Dr.K.Lenin. "DRAG & AVERSION PARTICLE SWARM OPTIMIZATION ALGORITHM FOR REDUCTION OF REAL POWER LOSS." International Journal of Research - Granthaalayah 5, no. 11 (2017): 168–76. https://doi.org/10.5281/zenodo.1069425.

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This paper projects Drag &amp; Aversion Particle Swarm Optimization (DAPSO) algorithm is applied to solve optimal reactive power problem. In DAPSO the idea of decreasing and increasing diversity operators used to control the population into the basic Particle Swarm Optimization (PSO) model. The modified model uses a diversity measure to have the algorithm alternate between exploring and exploiting behavior. The results show that both Drag &amp; Aversion Particle Swarm Optimization (DAPSO) prevents premature convergence to enhanced level but still keeps a rapid convergence. Proposed Drag &amp;
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Lenin, K. "ACTUAL POWER LOSS REDUCTION BY AUGMENTED PARTICLE SWARM OPTIMIZATION ALGORITHM." International Journal of Research -GRANTHAALAYAH 6, no. 9 (2018): 212–19. http://dx.doi.org/10.29121/granthaalayah.v6.i9.2018.1222.

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This paper presents an advanced particle swarm optimization Algorithm for solving the reactive power problem in power system. Bacterial Foraging Optimization Algorithm (BFOA) has recently emerged as a very powerful technique for real parameter optimization. In order to overcome the delay in optimization and to further enhance the performance of BFO, this paper proposed a new hybrid algorithm combining the features of BFOA and Particle Swarm Optimization (PSO) called advanced bacterial foraging-oriented particle swarm optimization (ABFPSO) algorithm for solving reactive power problem. The simul
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Dr., K. Lenin. "ACTUAL POWER LOSS REDUCTION BY AUGMENTED PARTICLE SWARM OPTIMIZATION ALGORITHM." International Journal of Research - Granthaalayah 6, no. 9 (2018): 212–19. https://doi.org/10.5281/zenodo.1442419.

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This paper presents an advanced particle swarm optimization Algorithm for solving the reactive power problem in power system. Bacterial Foraging Optimization Algorithm (BFOA) has recently emerged as a very powerful technique for real parameter optimization. In order to overcome the delay in optimization and to further enhance the performance of BFO, this paper proposed a new hybrid algorithm combining the features of BFOA and Particle Swarm Optimization (PSO) called advanced bacterial foraging-oriented particle swarm optimization (ABFPSO) algorithm for solving reactive power problem. The simul
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Dissertations / Theses on the topic "PSO (Particle Swarm Optimization) and Loss reduction"

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Ziari, Iman. "Planning of distribution networks for medium voltage and low voltage." Thesis, Queensland University of Technology, 2011. https://eprints.qut.edu.au/46684/1/Iman_Ziari_Thesis.pdf.

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Determination of the placement and rating of transformers and feeders are the main objective of the basic distribution network planning. The bus voltage and the feeder current are two constraints which should be maintained within their standard range. The distribution network planning is hardened when the planning area is located far from the sources of power generation and the infrastructure. This is mainly as a consequence of the voltage drop, line loss and system reliability. Long distance to supply loads causes a significant amount of voltage drop across the distribution lines. Capacitors
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Agbugba, Emmanuel Emenike. "Hybridization of particle Swarm Optimization with Bat Algorithm for optimal reactive power dispatch." Diss., 2017. http://hdl.handle.net/10500/23630.

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This research presents a Hybrid Particle Swarm Optimization with Bat Algorithm (HPSOBA) based approach to solve Optimal Reactive Power Dispatch (ORPD) problem. The primary objective of this project is minimization of the active power transmission losses by optimally setting the control variables within their limits and at the same time making sure that the equality and inequality constraints are not violated. Particle Swarm Optimization (PSO) and Bat Algorithm (BA) algorithms which are nature-inspired algorithms have become potential options to solving very difficult optimization problem
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Book chapters on the topic "PSO (Particle Swarm Optimization) and Loss reduction"

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Kanagasabai, Lenin. "Real Power Loss Reduction by Hybridization of Augmented Particle Swarm Optimization with Improved Crow Search Algorithm." In Lecture Notes in Electrical Engineering. Springer Singapore, 2022. http://dx.doi.org/10.1007/978-981-16-7472-3_15.

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Math, Rudrayya, and N. Kumar. "Optimal Siting and Sizing of DG Employing Multi-objective Particle Swarm Optimization for Network Loss Reduction and Voltage Profile Improvement." In Lecture Notes in Electrical Engineering. Springer Singapore, 2019. http://dx.doi.org/10.1007/978-981-13-5802-9_120.

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Abeyrathna, Kuruge Darshana, and Chawalit Jeenanunta. "Training Artificial Neural Networks With Improved Particle Swarm Optimization." In Handbook of Research on Advancements of Swarm Intelligence Algorithms for Solving Real-World Problems. IGI Global, 2020. http://dx.doi.org/10.4018/978-1-7998-3222-5.ch004.

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Particle Swarm Optimization (PSO) is popular for solving complex optimization problems. However, it easily traps in local minima. Authors modify the traditional PSO algorithm by adding an extra step called PSO-Shock. The PSO-Shock algorithm initiates similar to the PSO algorithm. Once it traps in a local minimum, it is detected by counting stall generations. When stall generation accumulates to a prespecified value, particles are perturbed. This helps particles to find better solutions than the current local minimum they found. The behavior of PSO-Shock algorithm is studied using a known: Schw
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Zhu, Xinyu, Tingting Wang, Hongliang Xiao, Jiangfeng Fu, and Xiaobo Zhang. "Optimization of Adaptive Cycle Engine Performance Based on Particle Swarm Optimization." In Advances in Transdisciplinary Engineering. IOS Press, 2025. https://doi.org/10.3233/atde250331.

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According to the aero-engine Performance Seeking Control (PSC) of Adaptive Cycle Engine (ACE) in multiple operating modes, an engine performance optimization control strategy based on Particle Swarm Optimization (PSO) is proposed. PSO algorithm has a simple structure and requires fewer parameter settings, and is suitable for ACE with many control variables. This method was applied to the ACE model with two optimization modes: maximum thrust model and minimum specific fuel consumption model. The simulation results showed 11.7% increase in maximum thrust mode and 0.13% reduction in minimum fuel
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Amhaimar, Lahcen, Ali Elyaakoubi, Mohamed Bayjja, Kamal Attari, and Saida Ahyoud. "A Comparison Study of PAPR Reduction in OFDM Systems Based on Swarm Intelligence Algorithms." In Search Algorithm - Essence of Optimization [Working Title]. IntechOpen, 2022. http://dx.doi.org/10.5772/intechopen.99396.

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Optimization algorithms have been one of the most important research topics in Computational Intelligence Community. They are widely utilized mathematical functions that solve optimization problems in a variety of purposes via the maximization or minimization of a function. The swarm intelligence (SI) optimization algorithms are an active branch of Evolutionary Computation, they are increasingly becoming one of the hottest and most important paradigms, several algorithms were proposed for tackling optimization problems. The most respected and popular SI algorithms are Ant colony optimization (
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Singh, Jagendra, Vinish Kumar, Kotha Sinduja, Pongkit Ekvitayavetchanukul, Atul Kumar Agnihotri, and Hazra Imran. "Enhancing Heart Disease Diagnosis Through Particle Swarm Optimization and Ensemble Deep Learning Models." In Advances in Computer and Electrical Engineering. IGI Global, 2024. https://doi.org/10.4018/979-8-3693-6834-3.ch010.

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The present research focused on combining Particle Swarm Optimization (PSO) based hybrid deep learning models to classify heart disease images and patient sequences. This study employs Convolutional Neural Networks (CNNs), including VGG 16, VGG 19 and ResNet 50, as well as Recurrent Neu-ral Networks (RNNs), whereby their performance is optimized by PSO to im-prove the accuracy in diagnosing heart disease from CT images together with associated medical history. The models experienced a significant increase in classification performance, using manual hyperparameters tuning by PSO. The combined a
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I., ManiKandan, T. Nagalakshmi, G. Vanya sree, Kaliappan Seeniappan, C. K. Arvinda Pandian, and S. Govinda Rao. "Optimizing Optical Fiber Path in Wavelength Division Multiplexing Networks Using Particle Swarm Optimization." In Metaheuristic and Machine Learning Optimization Strategies for Complex Systems. IGI Global, 2024. http://dx.doi.org/10.4018/979-8-3693-7842-7.ch016.

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In this paper, we explore the application of Particle Swarm Optimization (PSO) to maximize the performance of Wavelength Division Multiplexing (WDM) networks by optimizing optical fiber paths. Through rigorous evaluation metrics such as Data Transmission Speed Analysis and Congestion Reduction Assessment across ten trials, our findings reveal consistent and meaningful improvements. PSO effectively enhances data transfer speeds, resulting to more efficient network performance. Moreover, the approach reliably minimizes congestion levels, decreasing a significant challenge in WDM networks. These
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Ting, T. O. "Optimization of Drilling Process via Weightless Swarm Algorithm." In Emerging Research on Swarm Intelligence and Algorithm Optimization. IGI Global, 2015. http://dx.doi.org/10.4018/978-1-4666-6328-2.ch008.

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In this chapter, the main objective of maximizing the Material Reduction Rate (MRR) in the drilling process is carried out. The model describing the drilling process is adopted from the authors' previous work. With the model in hand, a novel algorithm known as Weightless Swarm Algorithm is employed to solve the maximization of MRR due to some constraints. Results show that WSA can find solutions effectively. Constraints are handled effectively, and no violations occur; results obtained are feasible and valid. Results are then compared to previous results by Particle Swarm Optimization (PSO) al
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Biswas, Kallol, Pandian M. Vasant, Moacyr Batholomeu Laruccia, José Antonio Gámez Vintaned, and Myo M. Myint. "Review on Particle Swarm Optimization Approach for Optimizing Wellbore Trajectory." In Advances in Systems Analysis, Software Engineering, and High Performance Computing. IGI Global, 2020. http://dx.doi.org/10.4018/978-1-7998-1192-3.ch017.

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Due to a variety of possible good types and so many complex drilling variables and constraints, optimization of the trajectory of a complex wellbore is very challenging. There are several types of wells, such as directional wells, horizontal wells, redrilling wells, complex structure wells, cluster wells, and extended reach wells. This reduction of the wellbore length helps to establish cost-effective approaches that can be utilized to resolve a group of complex trajectory optimization challenges. For efficient performance (i.e., quickly locating global optima while taking the smallest amount
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Liu Yuan, Wu Xing, Guo Yike, Xie Shaorong, Pu Huayan, and Peng Yan. "The Multiple Unmanned Surface Vehicles Cooperative Defense Based on PM-PSO and GA-PSO in the Sophisticated Sea Environment." In Frontiers in Artificial Intelligence and Applications. IOS Press, 2018. https://doi.org/10.3233/978-1-61499-900-3-801.

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The unmanned surface vehicles (USVs) have become a major trend in the construction of naval equipment and its flexibility and intelligence making it widely used in real-scenes. For cooperative defense with multiple USVs to intercept intruders, it is proposed that planning the path with obstacle avoidance and protecting the target by task allocation actions. The particle swarm optimization based on probe mechanism (PM-PSO) is proposed for pathing planning with obstacle avoidance. With the consideration of the constraints of different defense schemes such as the path cost, the interception loss,
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Conference papers on the topic "PSO (Particle Swarm Optimization) and Loss reduction"

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hussein shakah, Ghazi, and Mohammad Anwar Assaad. "Optimizing Health Pattern Recognition: A Fuzzy C-Means and Particle Swarm Optimization Approach for Enhanced Neural Network Performance." In 5TH INTERNATIONAL CONFERENCE ON COMMUNICATION ENGINEERING AND COMPUTER SCIENCE (CIC-COCOS'24). Cihan University-Erbil, 2024. http://dx.doi.org/10.24086/cocos2024/paper.1510.

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Health pattern recognition is vital for advancing personalized healthcare interventions. This research introduces a synergistic approach, combining Fuzzy C-Means clustering with Particle Swarm Optimization (PSO), to optimize the hyperparameters of an Artificial Neural Network (ANN) and enhance health pattern recognition. Leveraging key features such as 'Smoker,' 'BMI,' and 'GenHlth,' Fuzzy C-Means reveals distinctive health clusters, providing nuanced insights into diverse health profiles within the dataset. Subsequently, the PSO algorithm systematically optimizes critical ANN hyperparameters,
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Yin, BangTang, Cheng Chen, ChaoJie Jia, Kai Feng, Zhiyuan Wang, and Baojiang Sun. "A Diagnosis Method for Lost Circulation Based on Improved Symbolic Aggregate Approximation and Support Vector Machine." In International Petroleum Technology Conference. IPTC, 2025. https://doi.org/10.2523/iptc-25016-ms.

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Abstract In the process of ultra-deepwater drilling, the safe density window is narrow, and there are issues with large temperature differences and alternating high and low temperatures, making wellbore pressure control challenging and highly prone to complex lost circulation incidents. To achieve safe and efficient drilling, accurate identification and timely monitoring of complex lost circulation incidents are essential. To address the current issues of insufficient real-time performance and accuracy in lost circulation monitoring for ultra-deepwater drilling, this paper proposes a multi-par
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Kakkar, Deepti, Ashish Prashar, Mehar Latif, Aitraiyee Konar, Kishan Kumar, and Amitabh Tripathi. "HATA Path Loss Model Optimization Using Particle Swarm Algorithm." In International Conference on Women Researchers in Electronics and Computing. AIJR Publisher, 2021. http://dx.doi.org/10.21467/proceedings.114.37.

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Fallacious path loss predictions before the placing of base station(BS) cause under evaluation of circulation areas which gives rise to unabating call drops &amp; cross talks. The escalating demands of meeting overhaul needs of applications by users makes the consequence worst, which significantly influences competence of the cellular wireless system. Propagation model is a keystone of coverage planning. To slash cost, proper planning is needed in coverage of network in order to upgrade the quality of service. Now, K factor is taken into account in order to improve or enhance propagation model
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Ristić, Ognjen, and Nataša Trišović. "TRUSS STRUCTURE OPTIMIZATION USING PARTICLE SWARM OPTIMIZATION WITH DIRECT FEM COUPLING." In 10th International Congres of the Serbian Society of Mechanics. Serbian Society of Mechanics, Belgrade, 2025. https://doi.org/10.46793/icssm25.238r.

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This paper presents a methodology for the mass optimization of truss structures using Particle Swarm Optimization (PSO) coupled with Finite Element Method (FEM) analysis. The approach allows for the modification of nodal coordinates while maintaining structural integrity under specified loading conditions. The results indicate that the PSO algorithm effectively navigates the design space to identify optimal node positions that minimize structural mass while maintaining performance requirements. This methodology offers a promising approach for structural design in engineering applications where
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Salah, Khaled. "A generic model order reduction technique based on Particle Swarm Optimization (PSO) algorithm." In IEEE EUROCON 2017 -17th International Conference on Smart Technologies. IEEE, 2017. http://dx.doi.org/10.1109/eurocon.2017.8011103.

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Zhaoyun, Song, Bo Liu, Mao Xiaochen, and Lu Xiaofeng. "Optimization of Tandem Blade Based on Improved Particle Swarm Algorithm." In ASME Turbo Expo 2016: Turbomachinery Technical Conference and Exposition. American Society of Mechanical Engineers, 2016. http://dx.doi.org/10.1115/gt2016-56901.

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To improve the design quality of high-turning tandem blade, a coupling optimization system for the shape and relative position of tandem blades was developed based on an improved particle swarm optimization algorithm and NURBS parameterization. First of all, to increase convergence speed and avoid local optima of particle swarm optimization (PSO), an improved particle swarm optimization (IPSO) is formulated based on adaptive selection of particle roles, adaptive control of parameters and population diversity control. Then experiments are carried out using test functions to illustrate the perfo
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Vaisakh, K., M. Sridhar, and K. S. Linga Murthy. "Differential evolution particle swarm optimization algorithm for reduction of network loss and voltage instability." In 2009 World Congress on Nature & Biologically Inspired Computing (NaBIC). IEEE, 2009. http://dx.doi.org/10.1109/nabic.2009.5393308.

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Danila Shirly., A. R., M. Sudhilaya, Y. Priyadharshini, J. Shamni, and J. Poorani. "Improving Efficiency and Power Loss Minimization in Landsman DC-DC Converter using Particle Swarm optimization Technique (PSO)." In 2021 2nd International Conference for Emerging Technology (INCET). IEEE, 2021. http://dx.doi.org/10.1109/incet51464.2021.9456156.

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Karaaom, Chatuphat, Peerapol Jirapong, Panida Thararak, and Keerachat Tantrapon. "Optimal Allocation of Tie Switch in Distribution Systems for Energy Loss Reduction Using Particle Swarm Optimization." In 2020 8th International Electrical Engineering Congress (iEECON). IEEE, 2020. http://dx.doi.org/10.1109/ieecon48109.2020.229528.

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Khalil, Tamer M., and Alexander V. Gorpinich. "Optimal conductor selection and capacitor placement for loss reduction of radial distribution systems by selective particle swarm optimization." In 2012 Seventh International Conference on Computer Engineering & Systems (ICCES). IEEE, 2012. http://dx.doi.org/10.1109/icces.2012.6408516.

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