TY - JOUR ID - 21065 TI - Quasi-oppositional symbiotic organisms search algorithm for different economic load dispatch problems JO - Scientia Iranica JA - SCI LA - en SN - 1026-3098 AU - Das, D. AU - Bhattacharya, A. AU - Narayan Ray, R. AD - Department of Electrical Engineering, National Institute of Technology, Agartala, Pin-799046, India Y1 - 2020 PY - 2020 VL - 27 IS - 6 SP - 3096 EP - 3117 KW - Economic Load Dispatch KW - Opposition-based learning KW - Prohibited operating zone KW - Symbiotic Organisms Search KW - Valve point loading DO - 10.24200/sci.2018.50766.1855 N2 - In this paper, an effective meta-heuristic technique called Quasi-Oppositional Symbiotic Organisms Search is applied for solving non-convex economic dispatch problems. Symbiotic Organisms Search is a soft computing technique, inspired by organisms in the ecosystem. This technique is implemented for improving the solution quality in minimum time. In order to improve convergence rate, quasi-reflected numbers are used here instead of pseudo-random numbers. Different equality and inequality constraints such as transmission loss, load demand, prohibited operating zone, generator operating limits and boundary of ramp rate are considered here. Presence of multiple fuels and valve point are also considered in some cases. This algorithm is applied to four different test systems. Simulation results are compared with many recently developed optimization techniques to show the superiority and consistency of this method. Simulation results also show that the computational efficiency of this algorithm is much better than the other meta-heuristic methods available in the literature. UR - https://scientiairanica.sharif.edu/article_21065.html L1 - https://scientiairanica.sharif.edu/article_21065_361b2b5f54ad164e521e11d217387929.pdf ER -