In order to introduce and maintain an optimum balance between exploration and exploitation in the search space, a hybrid crossover oriented Particle Swarm optimization(PSO) and Grey Wolf optimization(GWO) based co-evolution structure, abbreviated as HC-PSOGWO is proposed in this paper. The proposed HC-PSOGWO mainly focuses on better generalization, search procedure and diversification. Here both particles and wolves, as search agents, concurrently and independently explore the entire search space for optimal results. The learning procedure of GWO is also modified. Once, the exploration process is over, the evolved search agents from both the optimization techniques are crossed, they communicate among themselves, better agents are identified in the process and they are again allowed to modify the swarms targeted by the individual technique. The performance of the proposed technique is tested on 23 well known classical benchmark functions, one real world optimization problem and compared with several established optimization techniques. The experimental results and related analysis show that the proposed HC-PSOGWO is significantly better than other existing techniques. © 2019 IEEE.