The nature of statistical learning theory
The nature of statistical learning theory
A species conserving genetic algorithm for multimodal function optimization
Evolutionary Computation
EPSO - best-of-two-worlds meta-heuristic applied to power system problems
CEC '02 Proceedings of the Evolutionary Computation on 2002. CEC '02. Proceedings of the 2002 Congress - Volume 02
Particle swarm optimisation with spatial particle extension
CEC '02 Proceedings of the Evolutionary Computation on 2002. CEC '02. Proceedings of the 2002 Congress - Volume 02
Locating and tracking multiple dynamic optima by a particle swarm model using speciation
IEEE Transactions on Evolutionary Computation
Hi-index | 0.00 |
Aiming at the precocious convergence problem of particle swarm optimization algorithm, adaptive particle swarm optimization (APSO) algorithm was presented. In this algorithm, the notion of species was introduced into population diversity measure. The species technique is based on the concept of dividing the population into several species according to their similarity. The inertia weight was nonlinearly adjusted by using population diversity information at each iteration step. Velocity mutation operator and position crossover operator were both introduced and the global performance was clearly improved. The APSO algorithm was adapted to search the optimal parameters of support vector machine (SVM) to increase the accuracy of SVM. A novel short-term load forecasting model based on SVM with APSO algorithm (APSO-SVM) is presented. The proposed model was tested on a certain electricity load forecasting problem. The empirical results illustrated that the new APSO-SVM model outperformed SVM, BPNN and regression model and can successfully identify the optimal values of parameters of SVM with the lowest prediction error values in load forecasting. Therefore, this model is efficient and practical during a short-term load forecasting of electric power system.