showing 2 related works from this author
Training Artificial Neural Networks With Improved Particle Swarm Optimization
2020
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: Schwefel's function. With promising performance on the Schwefel's function, PSO-Shock algorithm is util…
Hybrid Particle Swarm Optimization With Genetic Algorithm to Train Artificial Neural Networks for Short-Term Load Forecasting
2019
This research proposes a new training algorithm for artificial neural networks (ANNs) to improve the short-term load forecasting (STLF) performance. The proposed algorithm overcomes the so-called training issue in ANNs, where it traps in local minima, by applying genetic algorithm operations in particle swarm optimization when it converges to local minima. The training ability of the hybridized training algorithm is evaluated using load data gathered by Electricity Generating Authority of Thailand. The ANN is trained using the new training algorithm with one-year data to forecast equal 48 periods of each day in 2013. During the testing phase, a mean absolute percentage error (MAPE) is used …