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A novel statistical approach to numerical and multidisciplinary design optimization problems using pattern search inspired Harris hawks optimizer

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Abstract

Classical Harris Hawks optimiser (HHO) algorithm has a notable approach for global optimization. However, for constrained engineering optimization problems it is easily to get stuck in local search space. To step up the global search process of the current Harris hawks optimiser and hang it out of the local search space, the research framework purpose is to identify the discovery process of the current optimiser; the Harris hawks optimiser novel version was implemented using the pattern search algorithm named as the hybrid Harris hawks pattern search algorithm (hHHO-PS). The efficiency of approached optimiser has also been evaluated for different problems of non-convex, nonlinear and highly constrained engineering optimal complications. To confirm performance of suggested algorithm, consideration was given to 23 standard CEC2005 benchmark issues and nine multidisciplinary engineering design optimization problems. After testing, the efficacy of approaching hHHO-PS optimization algorithm has been found to be much stronger than the traditional Harris hawks optimiser, gray wolf optimiser, ant lion optimiser and moth flame optimization and other currently documented heuristics, metaheuristics and hybrid form optimization approaches, and the suggested methodology endorses its efficacy in problems of multidisciplinary nature and engineering optimization.

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Acknowledgements

The corresponding author wishes to thank Dr. O.P. Malik, Professor Emeritus, Department of Electrical and Computer Engineering, Schulich School of Engineering, University of Calgary, Calgary, Alberta, CANADA for continuous support, guidance, encouragement and for providing advance research facilities for post-doctorate research at the University of Calgary, Alberta, CANADA.

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Krishna, A.B., Saxena, S. & Kamboj, V.K. A novel statistical approach to numerical and multidisciplinary design optimization problems using pattern search inspired Harris hawks optimizer. Neural Comput & Applic 33, 7031–7072 (2021). https://doi.org/10.1007/s00521-020-05475-5

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