Performance evaluation of a novel self-tuning particle swarm optimization algorithm-based maximum power point tracker for porton exchange membrane fuel cells under different operating conditions

Refaat, Ahmed; Elbaz, Ahmed; Khalifa, Abd-Elwahab; Mohamed Elsakka, Mohamed; Kalas, Ahmed; Hegazy Elfar, Medhat

doi:10.1016/j.enconman.2023.118014

Cited by 14 publications

(2 citation statements)

References 49 publications

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“…In order to overcome the problem of premature convergence, Du et al [30] used chaotic sequences to reinitialize particles trapped in local optima and facilitate their escape. To prevent premature convergence of the PSO algorithm, Abbas et al [31] integrated chaos into their search process. There have been limited studies on the influence of chaotic search on multi-objective evolutionary algorithms.…”

Section: Current Status Of Research On Multi-objective Particle Swarm...mentioning

confidence: 99%

Multi-Objective Optimal Scheduling of Microgrids Based on Improved Particle Swarm Algorithm

Guan,

Wang,

et al. 2024

Energies

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Microgrid optimization scheduling, as a crucial part of smart grid optimization, plays a significant role in reducing energy consumption and environmental pollution. The development goals of microgrids not only aim to meet the basic demands of electricity supply but also to enhance economic benefits and environmental protection. In this regard, a multi-objective optimization scheduling model for microgrids in grid-connected mode is proposed, which comprehensively considers the operational costs and environmental protection costs of microgrid systems. This model also incorporates improvements to the traditional particle swarm optimization (PSO) algorithm by considering inertia factors and particle adaptive mutation, and it utilizes the improved algorithm to solve the optimization model. Simulation results demonstrate that this model can effectively reduce electricity costs for users and environmental pollution, promoting the optimized operation of microgrids and verifying the superior performance of the improved PSO algorithm. After algorithmic improvements, the optimal total cost achieved was CNY 836.23, representing a decrease from the pre-improvement optimal value of CNY 850.

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Section: Current Status Of Research On Multi-objective Particle Swarm...mentioning

confidence: 99%

Multi-Objective Optimal Scheduling of Microgrids Based on Improved Particle Swarm Algorithm

Guan,

Wang,

et al. 2024

Energies

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“…Liu et al [21] proposed Adaptive Weighted Particle Swarm Optimization (AWPSO) to improve the convergence velocity of the algorithm. Ahmed et al [22] proposed a novel tracking technique based on Self-Tuning Particle Swarm Optimization (ST-PSO). This method maximizes power output from the power generated by a protonexchange membrane fuel cell.…”

Section: Introductionmentioning

confidence: 99%

Hybrid Sine Cosine and Particle Swarm Optimization Algorithm for High-Dimensional Global Optimization Problem and Its Application

Wang,

Gao,

2024

Mathematics

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Particle Swarm Optimization (PSO) is facing more challenges in solving high-dimensional global optimization problems. In order to overcome this difficulty, this paper proposes a novel PSO variant of the hybrid Sine Cosine Algorithm (SCA) strategy, named Velocity Four Sine Cosine Particle Swarm Optimization (VFSCPSO). The introduction of the SCA strategy in the velocity formulation ensures that the global optimal solution is found accurately. It increases the flexibility of PSO. A series of experiments are conducted on the CEC2005 test suite with compositional algorithms, algorithmic variants, and good intelligent algorithms. The experimental results show that the algorithm effectively improves the overall performance of compositional algorithms; the Friedman test proves that the algorithm has good competitiveness. The algorithm also performs better in PID parameter tuning. Therefore, the VFSCPSO is able to solve the high-dimensional global optimization problems in a better way.

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A Kepler optimization algorithm improved using a novel Lévy-Normal mechanism for optimal parameters selection of proton exchange membrane fuel cells: A comparative study

Abdel-Basset,

Mohamed,

Sallam

et al. 2024

Energy Reports

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Performance evaluation of a novel self-tuning particle swarm optimization algorithm-based maximum power point tracker for porton exchange membrane fuel cells under different operating conditions

Cited by 14 publications

References 49 publications

Multi-Objective Optimal Scheduling of Microgrids Based on Improved Particle Swarm Algorithm

Multi-Objective Optimal Scheduling of Microgrids Based on Improved Particle Swarm Algorithm

Hybrid Sine Cosine and Particle Swarm Optimization Algorithm for High-Dimensional Global Optimization Problem and Its Application

A Kepler optimization algorithm improved using a novel Lévy-Normal mechanism for optimal parameters selection of proton exchange membrane fuel cells: A comparative study

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