Hybrid optimization: combining global and local search algorithms for efficient non-convex optimization

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Hybrid algorithms enhance convergence and efficiency in non-convex optimization, combining global and local search methods.

Hybrid algorithms improve accuracy and speed in non-convex optimization tasks, outperforming traditional techniques.
Results reveal that the combination of Genetic Algorithms and local search methods offers significant computational advantages.
Evaluation on benchmark functions demonstrates the effectiveness of hybrids like GA + Newton and PSO + CG.
The study builds on prior research by blending global search strategies to develop more efficient optimization solutions.

Mahmoud El-Hashash (2025) studied this question.

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