摘要： Maximum power point tracker (MPPT) techniques have been used to extract the maximum power available form photovoltaic (PV) energy systems. Conventional MPPT techniques like perturb and observe (P&O), hill climbing (HC), incremental conductance etc. were good enough to track the maximum power for the unshaded PV systems because it has only one power peak in the P-V curve. In the case of partial shading conditions (PSC), many peaks are created; one global maximum power point (GMPP) and many local maximum power points (LMPPs). Most of conventional MPPT techniques may stick to one of the LMPPs, which reduce the MPPT efficiency of PV systems. Soft computing techniques like particle swarm optimization (PSO), gray wolf optimization (GWO), and Cuckoo search optimization (CSO) etc. can catch the GMPP of PV system under the same PSC. These latter techniques suffer from two problems, the first problem is the high oscillations around the GMPP, the second problem is that, they cannot follow the new GMPP once it changed its position due to the searching agents will be busy around old GMPP caught. The solution of these two problems are the motivation of this research. GWO has been used to catch the GMPP and the problem of oscillations around the GMPP has been solved by hybridizing this technique with fuzzy logic controller (FLC) for soft tune the output generated power at the GMPP. The FLC characterizes by accurate GMPP catching with almost zero oscillations. The second problem is solved in this paper by re-initializing the GWO with two new initialization techniques. The results obtained from GWO-FLC with two different re-initialization techniques have been compared to the results of PSO without reinitializing its particles. The results obtained from this work prove the superior performance of the new proposed technique in terms of dynamic GMPP catching and MPPT power efficiency in case of time variant PSCs.