Cognitive networks are stands out as intelligent technology which evolved to enhance spectrum utilization. Secondary users are allowed to utilize the primary user's frequency bands on idling times. Identifying the idle licensed spectrum is achieved through spectrum sensing. The spectrum holes should be explored such that a suitable spectrum can be selected and allocated to the secondary users. Existing spectrum sensing and selection schemes have limitations due to interferences. Thus, an optimization algorithm based on bioinspired improved weed optimization was presented in this research work for enhanced channel utilization. The optimization model explores the channel characteristics and reduces the primary network interferences through its optimal solution. Further, Markov greedy-based auction scheme was presented for channel allocation. Considering the channel capacity, delay, and switching rates the allocation is performed to enhance the overall system performance. Simulation analysis demonstrates the superior performance of the proposed model over existing techniques like particle swarm optimization and genetic algorithm.