SHAW-PSO method flow chart.Yang, QidongZuo, HongchaoLi, Weidong
The increasing demand for electricity presents substantial challenges in power system planning, particularly optimizing the Optimal Power Flow (OPF) problem. The OPF problem entails establishing the best settings for control variables in a power system t
This study investigates the no-wait flow shop scheduling problem and proposes a hybrid (HES-IG) algorithm that utilizes makespan as the objective function. To address the complexity of this NP-hard problem, the HES-IG algorithm combines evolution strategies (ES) and iterated greedy (IG) algorithm...
such as Particle Swarm Optimization (PSO) [11] and Differential Evolution (DE) [12], to newer approaches such as RIME optimization algorithm (RIME) [13], Weighted Mean of Vectors (INFO) [14], Runge Kutta Optimizer (RUN) [15], Hunger Games Search (HGS) [16], Harris Hawks Optimization ...
For the training data set we used randperm function to determine the amount of gas for each cell. In this case, we destroyed the pattern learning in ANFIS method and force this algorithm to learn the data set based on a fully random framework. The randomization is implemented throughout the...
They assessed the performance of the cuckoo optimization algorithm (COA) and PSO algorithm coupled with the MLP network. Among various influential parameters, five variables were chosen as input parameters, including RPM, WOB, flow rate, compressional wave slowness, and shear wave slowness. The ...
This finding could also highlight the importance of sensitivity analysis of tuning parameters to achieve an accurate model with a cost-effective computational run.Similar content being viewed by others Investigation on performance of particle swarm optimization (PSO) algorithm based fuzzy inference system...
The advantage of linear prediction algorithm is that the calculation complexity is low, but the effect is poor when dealing with complex passenger flow data. The nonlinear prediction model can deal with the volatility of passenger flow time series, but it has the shortcomings of complex theory and...
6 Comparison chart of TaoRanTing passenger prediction RMSE = 1 n n (yt − yˆt )2 i=1 (6) 1n MAE = n yt − yˆt i=1 (7) MAPE = 1 n (yt − yˆt ) n i=1 yt × 100% (8) In which, yt indicates the actual observed value of the passenger flow, y...
Precast production Flowshop scheduling Optimization Genetic algorithm 1. Introduction Precast concrete structures have demonstrated better production efficiency and construction quality by employing highly effective manufacturing process compared to the in-site concrete structures. The components of this kind of ...