A Hybrid Quadratic Programming and Evolutionary Single-objective Optimization Algorithm: Empirical Study on CEC 2022 Benchmark Problems
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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.
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