Group search optimizer with intraspecific competition and lévy walk

Optimal reactive power dispatch with wind power integrated using group search optimizer with intraspecific competition and lévy walk

“…where P Gi and P Di are the injected and demanded active power at bus i; Q Gi and Q Di are the injected and demanded reactive power; B ij and G ij are the transfer susceptance and conductance between bus i and j; N i stands for the total number of buses adjacent to bus i, including bus i. h(u,x) represents working limits of generators, power transformers and reactive power sources, and emphasizes power system security constraints, such as limits on bus voltages and branch apparent power flow. The details can be referred to [8].…”

“…It leads to more intense scrabble for the resource lying in the position of the producer and more rangers who will disperse with longer step by LW in the searching space. In [8], it has been proved that IC makes the scrounging coefficient and rangers' ratio vary adaptively to balance local and global searching, and LW stimulates rangers to perform more efficient global searching than RW. In other words, GSO achieves better performance than GSO in that it improves GSO's local searching ability while maintaining its global searching performance.…”

“…However, GSO's local searching ability is not satisfactory, as shown in its modest performance on optimizing unimodal functions [7]. In order to improve GSO's local searching ability while maintaining its merit in global searching, we have proposed an improved GSO, group search optimizer with intraspecific competition and lévy walk (GSOICLW) [8], incorporating intraspecific competition (IC) [9] and lévy walk (LW) [10] into the GSO model. IC makes the scroungers strengthen local searching while stimulates more rangers to do global searching.…”

Wind-thermal power system dispatch using MLSAD model and GSOICLW algorithm

“…However, it shows modest performance on optimizing unimodal functions [9], which demonstrates the local searching ability should be enhanced. Therefore, an improved GSO, GSOICLW, has been proposed to improve its local searching ability [10]. In reference [10], GSOICLW is verified to show better performances than that of GSO by testing against standard benchmark functions.…”

“…Therefore, an improved GSO, GSOICLW, has been proposed to improve its local searching ability [10]. In reference [10], GSOICLW is verified to show better performances than that of GSO by testing against standard benchmark functions.…”