**6. Conclusion**

In this study, an effective GWO algorithm is united with quasi-oppositional based learning (QOGWO) has been effectively implemented to solve a hydrothermal test system with quadratic nonlinear cost functions. Progressive improvement of the computational efficiency and better convergence characteristics are attained by quasioppositional based learning introduced in the conventional GWO algorithm. It is observed that the simulation time for the same number of iterations and the net cost of generation got by the presented QOGWO for the day is lower than others in all the


*Swarm Intelligence - Recent Advances and Current Applications*

**Table 3.**

*Hourly hydro discharges ( 104 m3) of the test system in case-3.*
