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It can be difficult to optimize complex issues, and doing so frequently calls for the application of cutting-edge methods like mathematical modelling and evolutionary algorithms. Our proposal in this work is to address complex optimization issues using a hybrid strategy that integrates both approaches. The suggested method builds a surrogate model of the issue by mathematical modelling, which is subsequently optimized through the application of evolutionary algorithms. The hybrid methodology is tested against other optimization methods, such as particle swarm optimization and genetic algorithms, on a series of benchmark tasks. The experimental findings demonstrate that in terms of both computing time and solution quality, the suggested hybrid strategy performs better than various alternative methods. The suggested methodology exhibits great potential as a means of resolving intricate optimization issues across diverse fields, such as engineering, finance, and healthcare.
Ogala et al. (Sun,) studied this question.