The focus of this dissertation is on the development of hybrid nature inspired metaheuristics for engineering design optimization problems. In this study, three nature inspired metaheuristics naming Artificial Showering Algorithm (ASHA), Artificial Bee Colony (ABC) algorithm and Differential Evolution (DE) have been considered for improvement and hybridization. We propose several improved as well as novel mixtures of the Nature Inspired Computational (NIC) methods, such as Targeted Showering Optimization (TSO), Radial ABC (RABC), hybrid of ABC and a modified ASHA (ABC-MASH) and Differential Targeted ABC (DTABC) algorithms. The structures and working principles of the proposed algorithms are discussed and analyzed in details. The performance of the our proposed hybrid NIC algorithms has been investigated by statistical analysis of their results on nonlinear, unimodal, multi-modal, multi-objective, nonlinear systems in engineering and engineering design optimization problems. The analysis reveals that the proposed hybrid NIC algorithms overcome the deficiencies of individual algorithms and outperform several past hybrid methods on engineering design optimization problems. It has been established through computer simulations and non-parametric analysis of the results that our designed hybrid NIC algorithms are consistent in producing superior optimization results over the standard individual NIC algorithms as well as the past hybrid methods with respect to the exploration efficiency, speed of convergence and quality and quantity of the best and mean optimal solutions attained.