Parameter identification of engineering problems using a differential shuffled complex evolution

An accurate mathematical model has a vital role in controlling and synchronization of different systems. But generally in real-world problems, parameters are mixed with mismatches and distortions. In this paper, an improved shuffled complex evolution (SCE) is proposed for parameter identification of engineering problems. The SCE by employing parallel search efficiently finds neighborhoods of the optimal point. So it carries out exploration in a proper way. But its drawback is due to exploitation stages. The SCE cannot converge accurately to an optimal point, in many cases. The current study focuses to overcome this drawback by inserting a shrinkage stage to an original version of SCE and presents a powerful global numerical optimization method, named the differential SCE. The efficacy of the proposed algorithm is first tested on some benchmark problems. After achieving satisfactory performance on the test problems, to demonstrate the applicability of the proposed algorithm, it is applied to ten identification problems includes parameter identification of ordinary differential equations and chaotic systems. Practical experiences show that the proposed algorithm is very effective and robust so that it produces similar and promising results over repeated runs. Also, a comparison against other evolutionary algorithms reported in the literature demonstrates a significantly better performance of our proposed algorithm.