A Hybrid Quadratic Programming and Evolutionary Single-objective Optimization Algorithm: Empirical Study on CEC 2022 Benchmark Problems
Abstract views: 20
/ 
PDF downloads: 24
Keywords:
Optimization, SQP, GA, Objective Function, Single-Objective Constrained OptimizationAbstract
Optimization methods are used in many fields of study to find solutions that maximize or
minimize some operating parameters. Optimization can be considered constrained or unconstrained, as well
as computational and traditional optimization algorithms. Both has advantages and disadvantages among
them. Therefore, to improve the performance of the algorithm it is possible to use both in a hybrid manner.
In this research, hybrid computational and traditional optimization method is proposed. For this purpose,
two algorithms are selected as the examples of both categories, which are as a mathematical algorithm
Sequential Quadratic Programming (SQP) and as a metaheuristic algorithm Genetic Algorithm (GA). As
hybrid algorithm whose are named as SQP-GA and GA-SQP, are used. In addition to GA-SQP hybrid
algorithm which is composed of two different forms named as V1 and V2 with respect to the collaboration
of these algorithms. In this paper, this proposed hybrid algorithms were applied to the CEC 2022 benchmark
problems are used to solve with boundary constrained optimization.
Downloads
References
J. R. R. A. Martins and A. Ning, Engineering Design Optimization, Cambridge University Press, 2021-10-01.
D. Vora and R. Kamatchi, Deep Learning in Engineering Education: Performance Prediction Using Cuckoo-Based Hybrid Classification, Machine Learning and Deep Learning in Real-Time Applications, 2020.
A. Lambora, K. Gupta and K. Chopra, Genetic Algorithm- A Literature Review, Faridabad, India: 2019 International Conference on Machine Learning, Big Data, Cloud and Parallel Computing (COMITCon), 2019.
P. B. Thanedar, J. S. Arora, C. H. Tseng, O. K. Lim and G. J. Park, Performance of some SQP algorithms on structural design problems, Wiley, 1986.
V. Plevris and G. Solorzano, "A collection of 30 multidimensional functions for global optimization benchmarking.," MDPI Open Access Journals, pp. 7(4),46, 2022.
Abhishek Kumar, Kenneth V. Price, Ali Wagdy Mohamed, Anas A. Hadi, P. N. Suganthan, "Problem Definitions and Evaluation Criteria for the CEC 2022 Special Session and Competition on Single Objective Bound Constrained Numerical Optimization," December 2021.
K Schittkowski, YX Yuan, "Sequential Quadratic Programming Methods," Wiley Encyclopedia of Operations Research and Management Science, 2010.
V.-D Nguyen, L. Wu, F. Remacle, and L. Noels, “A quantum annealing-sequential quadratic programming assisted finite element simulation for non-linear and history-dependent mechanical problems,” European Journal of Mechanics - A/Solids, Vol. 105, 2024.
