Niching strategies for particle swarm optimization

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Particle swarm optimization : empirical and theoretical stability analysis

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A Generalized theoretical deterministic particle swarm model

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Particle swarm optimization and differential evolution for multi-objective multiple machine scheduling

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Short term load forecasting based on hybrid artificial neural networks and particle swarm optimisation by Shezi, Ellen

“…Short term load forecasts have been conducted by using various CI methods such as Artificial Neural Networks (ANNs), Genetic Algorithms (GAs), Fuzzy Logic (FL), Expert Systems (ES), and Particle Swarm Optimisation (PSO). Hybrid versions of these methods, where two or more CI methods are amalgamated in a process to forecast future load, have also been used. iv In this research, a traditional forecasting technique, Multiple Linear Regression (MLR), was compared with a CI technique, Artificial Neural Networks. …”
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Optimal placement of battery energy storage system for voltage profile improvement and power loss reduction by Skunana, Nkululeko Skunana

“…Initial assessments establish a baseline scenario without BESS, followed by the application of Particle Swarm Optimization (PSO) and Genetic Algorithm (GA) to identify optimal BESS locations. …”
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Predictive Maintenance of Wind Turbines: Remaining Useful Life Estimation Using a BPSO-Optimized Attention-Based CNN-BiLSTM Approach by Hawas, Eman Khaled

“…Additionally, feature selection was applied using Binary Particle Swarm Optimization (BPSO), where only the most essential features were selected as input to the CNN-BiLSTM model. …”
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Engineering Nature-Inspired Heuristics for the Open Shortest Path First Weight Setting Problem

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Detection and statistical modelling of output power ramp events for utility scale wind energy facilities by Lyners, Danielle

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Creating additional network capacity on constrained medium voltage networks utilizing distributed generation (specifically PV technology) by Ramdhin, Avinash

“…The non-linear solutions to the function results in optimizing the amount and allocation of distributed DG along the MV feeder hence creating additional capacity on constrained networks. Particle Swarm Optimization (PSO) was found to be best suited for this research due to its efficiency to solving non-linear optimization problems and is proposed as the appropriate method of optimally integrating DG to un- constrain MV networks. …”
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Multi-purpose applications of energy storage systems in a power system network by Okafor, Chukwuemeka Emmanuel

“…Additionally, by deploying the new metaheuristic algorithm, the DSO, the computational time was reduced compared with other metaheuristic algorithms such as the particle swarm optimization (PSO) and genetic algorithm (GA). …”
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Optimal allocation of distributed generation for power loss reduction and voltage profile improvement by Oluwole, Osaloni Oluwafunso

“…This research used Genetic Algorithm and Improved Particle Swarm Optimization (GA-IPSO) for optimal placement and sizing of DG for power loss reduction and improvement of voltage profile. …”
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Efficient radio resource management for the fifth generation slice networks by Oladejo, Sunday Oladayo

“…This thesis mainly addresses key challenges relating to efficient radio resource management. First, a particle swarm-intelligent profit-aware resource allocation scheme for a 5G slice network is proposed to prioritise the profitability of the network while at the same time ensuring that the QoS requirements of slice users are not compromised. …”
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State Estimation in Active Distribution Systems: Comparison Between Weighted Least Squares and Extended Kalman Filter Algorithms by Watitwa, Jeff Kimasere

“…The ADRESCONCEPT project data were used to simulate real-time measurements, while the Particle Swarm Optimization algorithm was used to place the DGs and PMUs on the best possible nodes of the selected test system. …”
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Energy management of micro-grid using cooperative game theory by Oladejo, Ismaheel Oyeyemi

“…This is in contrast with other heuristic algorithms such as genetic algorithm (GA) and particle swarm optimization (PSO) that require other control parameters (i.e. …”
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Deep Reinforcement Learning-Based Approaches for the MPPT Control of Standalone Solar PV Systems by Nwachukwu, Sampson

“…Recently, metaheuristic optimization algorithms, including particle swarm optimization (PSO), genetic algorithm (GA), etc., have been used to deal with the limitations of classical methods. …”
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