concept

particle swarm optimization

Also known as: PSO, PSO-based algorithm, particle swarm optimization algorithm

Facts (24)

Sources

A comprehensive overview on demand side energy management ... link.springer.com Springer Mar 13, 2023 14 facts

referenceMenos-Aikateriniadis, Lamprinos, and Georgilakis (2022) reviewed particle swarm optimization for residential demand-side management, specifically focusing on scheduling and control algorithms for demand response provision in the journal Energies.

referenceLee KY and Park J-B (2006) analyzed the advantages and disadvantages of applying particle swarm optimization to the economic dispatch problem in a paper presented at the 2006 IEEE PES Power Systems Conference and Exposition.

claimHussain et al. (2016) suggest a hybrid method that combines Particle Swarm Optimization (PSO) and Gray Wolf Optimization (GWO) using day-ahead scheduling.

referenceFaria et al. (2013) applied a modified particle swarm optimization algorithm to schedule integrated demand response and distributed generation (DG) resources.

referenceLogenthiran T, Srinivasan D, and Phyu E proposed using particle swarm optimization for demand side management in smart grids in a 2015 paper presented at the IEEE Innovative Smart Grid Technologies-Asia (ISGT ASIA) conference.

referenceFaria P, Soares J, Vale Z, Morais H, and Sousa T (2013) applied a modified particle swarm optimization algorithm to integrated demand response and distributed generation (DG) resources scheduling.

referenceRahman I, Vasant PM, Singh BSM, and Abdullah-Al-Wadud M published a study in 2016 titled 'On the performance of accelerated particle swarm optimization for charging plug-in hybrid electric vehicles' in the Alexandria Engineering Journal.

referenceRoy C and Das DK published a study in 2021 titled 'A hybrid genetic algorithm (GA)–particle swarm optimization (PSO) algorithm for demand side management in smart grid considering wind power for cost optimization' in the journal Sādhanā.

referenceYang H-T, Yang C-T, Tsai C-C, Chen G-J, and Chen S-Y (2015) developed an improved Particle Swarm Optimization (PSO) based home energy management system integrated with demand response for smart grids, presented at the 2015 IEEE Congress on Evolutionary Computation.

claimHussain et al. (2016) suggest a hybrid method that combines Particle Swarm Optimization (PSO) and Gray Wolf Optimization (GWO) using day-ahead scheduling.

referenceLee KY and Park J-B presented 'Application of particle swarm optimization to economic dispatch problem: advantages and disadvantages' at the 2006 IEEE PES Power Systems Conference and Exposition.

referenceZhao J, Wen F, Dong ZY, Xue Y, and Wong KP published 'Optimal dispatch of electric vehicles and wind power using enhanced particle swarm optimization' in IEEE Transactions on Industrial Informatics in 2012.

claimIn the context of energy management optimization, PSO stands for Particle swarm optimization.

A critical review on techno-economic analysis of hybrid renewable ... link.springer.com Springer Dec 6, 2023 6 facts

referenceTudu et al. (2012) analyzed the techno-economic feasibility of a hybrid renewable energy system using an improved version of particle swarm optimization.

claimTudu et al. [29] suggested an improved particle swarm optimization strategy to simplify challenging nonlinear optimization problems in renewable energy systems.

claimIn studies of combined heat and power systems for residential applications, the particle swarm optimization (PSO) algorithm produced cost-effective solutions more quickly than the genetic algorithm (GA), although the genetic algorithm provided more promising results.

claimCompound technology systems utilizing Particle Swarm Optimization (PSO) and fuzzy logic controllers can minimize costs and improve power generation by optimizing the Maximum Power Point Tracking (MPPT) algorithm in DC/DC converter designs.

measurementSalwe et al. performed Particle Swarm Optimization (PSO) and Genetic Algorithm (GA) experiments on a photovoltaic/biomass/air current compound power system, finding that continuous charging resulted in costs of $0.2625/kWh (GA) and $0.2617/kWh (PSO), while cycle charging resulted in costs of $0.2396/kWh (GA) and $0.2393/kWh (PSO).

procedureResearchers investigated the economic performance of combined heat and power systems for residential applications using iterative approaches, specifically mixed-integer linear programming models and heuristic techniques such as genetic algorithms (GA) and particle swarm optimization (PSO).

Comprehensive framework for smart residential demand side ... nature.com Nature Mar 22, 2025 1 fact

referenceThe research paper 'Improved salp swarm algorithm based on particle swarm optimization for feature selection' was published in J. Ambient Intell. Humaniz. Comput. 10, 3155–3169 in 2019.

Neuro-Symbolic AI: Explainability, Challenges, and Future Trends arxiv.org arXiv Nov 7, 2024 1 fact

referenceR. Saravanakumar, N. Krishnaraj, S. Venkatraman, B. Sivakumar, S. Prasanna, and K. Shankar developed a fault diagnosis model for rotating machinery that combines hierarchical symbolic analysis, particle swarm optimization, and deep neural networks, published in the journal Measurement in 2021.

Sustainable Energy Transition for Renewable and Low Carbon Grid ... frontiersin.org Frontiers Mar 23, 2022 1 fact

referenceViet, Phuong, Duong, and Tran published 'Models for Short-Term Wind Power Forecasting Based on Improved Artificial Neural Network Using Particle Swarm Optimization and Genetic Algorithms' in the journal Energies in 2020.

Demand side management using optimization strategies for efficient ... journals.plos.org PLOS ONE Mar 21, 2024 1 fact

referenceZaini F. A., Sulaima M. F., Razak I. A. W. A., Zulkafli N. I., and Mokhlis H. published 'A Review on the Applications of PSO-Based Algorithm in Demand Side Management: Challenges and Opportunities' in IEEE Access in 2023.