An Enhanced Harris Hawks Optimization Algorithm for High-Dimensional Optimization Problems

This chapter introduces an Enhanced Harris Hawks Optimization Algorithm (EHHA) designed to tackle high-dimensional optimization problems and constrained engineering design challenges. The original Harris Hawks Optimization (HHA) algorithm, inspired by the hunting behaviors of hawks, has been widely used but suffers from slow convergence. The EHHA addresses this limitation by introducing an enhanced transition phase and a new exploration coefficient, which improves the algorithm's efficiency and accuracy. The study evaluates the EHHA's performance on three high-dimensional test problems and compares it with existing algorithms, demonstrating superior convergence speed and computational efficiency. Additionally, the EHHA is applied to real-world engineering problems, such as spring design and welded beam design, where it outperforms other methods in finding optimal solutions. The results highlight the EHHA's potential for solving complex optimization problems in various industries, offering a robust and efficient alternative to traditional optimization techniques.