Odor source localization (OSL) in complex industrial environments remains a significant challenge due to the coupled effects of building occlusion and turbulent advection, which often lead to pseudo-source stagnation. This paper proposes an adaptive collaborative Genghis Khan shark optimizer (ACGKSO), a unified search framework designed for robust multi-robot OSL. Specifically, an adaptive perturbation strategy is introduced to balance global exploration and local exploitation. This is achieved through a dynamic step size mechanism driven by two factors: an exponentially decaying convergence rate and a linearly amplified term based on swarm dispersion. Concurrently, to counter premature convergence in complex environments, we introduce a dual-modal experience sharing mechanism. This approach enhances collective learning by integrating both individual historical best solutions and neighborhood optima. Experimental results validate the effectiveness and robustness of the proposed approach compared with other swarm intelligence methods. In single-interference scenarios, ACGKSO achieves a localization success rate of 94%, significantly outperforming the baseline algorithms. In more complex multi-interference environments, it maintains an 83% success rate and reduces the average number of search steps by 21.1% relative to the standard GKSO. These results demonstrate that our framework significantly outperforms other SI methods in terms of accuracy and efficiency across different environmental conditions.