Small and large scale engineering design optimization using an innovative hybrid algorithm

An innovative hybrid algorithm (i h PSODE) present in this paper, for finding the solution of small and large scale engineering design optimization problems. A new particle swarm optimization (nPSO) and differential evolution (nDE) introduced in i h PSODE, to promote the search accuracy. Besides, a novel inertia weight, acceleration factor and position update structure is adopted in nPSO to increase the population diversity as well as a novel mutation approach and crossover rate is implemented in nDE to escape from local optima. After fitness function calculations recognize the top half member with discard rest half and apply nPSO which help to sustain exploration and exploitation competency of the algorithm. Furthermore, to achieve rapid convergence and stability, apply nDE on offspring created by nPSO. The population resultant by nPSO and nDE are combined for repetition. The efficiency of i h PSODE with its integrating nPSO and nDE algorithm validated on 23 unconstrained benchmark functions and utilized to solve 5 small (structural optimization) and 1large (economic dispatch with 3-, 6-, 15-, 40-, 140-unit test system considering with or without valve point effect) scale engineering design optimization problems plus comparing the results with various modern algorithms. Experimental and equated results confirm the superiority of the proposed algorithms.