Particle swarm optimization vs genetic algorithm, application and comparison to determine the moisture diffusion coefficients of pressboard transformer insulation

Villarroel, R.; García, Diego; Dávila, Miguel Ángel Mendoza; Caicedo, Eduardo

doi:10.1109/tdei.2015.005123

Cited by 19 publications

(4 citation statements)

References 16 publications

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“…The short circuit currency, loss and impact are directly determined by the short circuit impedance. Based on Rogowski method [12], the leakage impedance can be written as:…”

Section: Short Circuit Equationmentioning

confidence: 99%

Three-phase Transformer optimization design based on NSGA2 algorithm

Li,

Zhang,

Chen

et al. 2023

J. Phys.: Conf. Ser.

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To solve the problems of lack of diversity and low quality of solution set in the solving process of traditional transformer optimization algorithm, a multi-objective optimization algorithm based on NSGA-II is proposed. Firstly, a multi-objective optimization model of power transformer winding was established with manufacturing cost, additional coil loss and transformer short-circuit impedance as optimization objectives. Secondly, NSGA-II algorithm was used to optimize the model design. The optimization results showed that, compared with traditional MOPSO, MODE and MOEA. NSGA-II algorithm had better fitness values for each target. Finally, the reliability of the scheme is proved in a 110 kV/63000 kVA prototype, which has better economy compared with the traditional scheme.

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“…The short circuit currency, loss and impact are directly determined by the short circuit impedance. Based on Rogowski method [12], the leakage impedance can be written as:…”

Section: Short Circuit Equationmentioning

confidence: 99%

Three-phase Transformer optimization design based on NSGA2 algorithm

Li,

Zhang,

Chen

et al. 2023

J. Phys.: Conf. Ser.

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“…Therefore, in GA the population evolves around a subset of the best individuals. • PSO has good cooperation between particles when we compare by GA, that is to say, particle swarms share their information [4,22,30,31]. • PSO is a parallel optimization strategy but GA is a serial strategy, and GA may be integrated into PSO.…”

Section: Strong Points Of Pso Compared With Gamentioning

confidence: 99%

Optimization of Welding Input Parameters Using PSO Technique for Minimizing HAZ Width in GMAW

Mezaache

Babes

Chaouch

2022

Period. Polytech. Mech. Eng.

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In order to conceive command systems for welding equipment based on intelligence techniques similar to human thinking; it is better to use artificial intelligence methods, for example: Genetic algorithms and particle swarm optimization. Freshly, this latter has received increased attention in many research fields. This paper discuss the application of particle swarm optimization algorithm to optimize the welding process parameters and obtain a better Width of Head Affected Zone (WHAZ) in the welding machine which is gas metal arc welding. The effect of four main welding variables in the gas metal arc welding process, namely welding speed, welding voltage, nozzle-to-plate distance and wire feed speed on the WHAZ are studied. A source code is developed in MATLAB 8.3 to perform the optimization.

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“…Their hybrid versions are also reported in the literature [7][8][9], where qualities of both are combined to solve a particular problem. They have also been employed individually on other problems [10,11] where PSO was found to have better performance than that of GA.…”

Section: Introductionmentioning

confidence: 99%

“…where j is particle counter, k is iteration counter, c 1 and c 2 are acceleration coefficients, r 1 and r 2 are random numbers in the range of 0-1, pbest j is the best position of particle based on its personal knowledge, gbest is the best position of particle based on group knowledge, and µ is the inertia weight given by Equation (11).…”

mentioning

confidence: 99%

Implementation and Comparison of Particle Swarm Optimization and Genetic Algorithm Techniques in Combined Economic Emission Dispatch of an Independent Power Plant

et al. 2019

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This paper presents the optimization of fuel cost, emission of NOX, COX, and SOX gases caused by the generators in a thermal power plant using penalty factor approach. Practical constraints such as generator limits and power balance were considered. Two contemporary metaheuristic techniques, particle swarm optimization (PSO) and genetic algorithm (GA), have were simultaneously implemented for combined economic emission dispatch (CEED) of an independent power plant (IPP) situated in Pakistan for different load demands. The results are of great significance as the real data of an IPP is used and imply that the performance of PSO is better than that of GA in case of CEED for finding the optimal solution concerning fuel cost, emission, convergence characteristics, and computational time. The novelty of this work is the parallel implementation of PSO and GA techniques in MATLAB environment employed for the same systems. They were then compared in terms of convergence characteristics using 3D plots corresponding to fuel cost and gas emissions. These results are further validated by comparing the performance of both algorithms for CEED on IEEE 30 bus test bed.

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Particle swarm optimization vs genetic algorithm, application and comparison to determine the moisture diffusion coefficients of pressboard transformer insulation

Cited by 19 publications

References 16 publications

Three-phase Transformer optimization design based on NSGA2 algorithm

Three-phase Transformer optimization design based on NSGA2 algorithm

Optimization of Welding Input Parameters Using PSO Technique for Minimizing HAZ Width in GMAW

Implementation and Comparison of Particle Swarm Optimization and Genetic Algorithm Techniques in Combined Economic Emission Dispatch of an Independent Power Plant

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