Integrated improved Harris hawks optimization for global and engineering optimization.

Area of Science:

• Computational Intelligence
• Swarm Intelligence
• Optimization Algorithms

Background:

• The original Harris hawks optimization (HHO) algorithm suffers from unstable optimization effects and a tendency to get stuck in local optima.
• Existing improved HHO algorithms often fail to adequately address the issue of escaping local optima.

Purpose of the Study:

• To propose an integrated improved Harris hawks optimization (IIHHO) algorithm designed to overcome the limitations of the original HHO.
• To enhance the algorithm's ability to escape local optima and improve overall optimization performance.

Main Methods:

• Introduced an intermittent energy regulator to adjust Harris hawks' energy, mimicking prey behavior and improving local search.
• Implemented a modified composite function to create a more regular attenuation vector, addressing random vector uncertainty.
• Clarified the search scope of Levy flight to facilitate escaping local optima.
• Incorporated the Cardano formula function to adjust step size, mitigating fixed step size limitations and boosting accuracy.

Main Results:

• The IIHHO algorithm demonstrated superior convergence values compared to most improved evolutionary algorithms on the CEC 2013 test set.
• Experiments on the CEC 2022 test set showed the IIHHO algorithm maintains a strong ability for optimal value searching against state-of-the-art algorithms.
• Application in engineering experiments confirmed the IIHHO algorithm's advantages in solving search space problems, evidenced by minimum cost calculations.

Conclusions:

• The proposed IIHHO algorithm effectively addresses the stagnation and local optimum issues of the original HHO.
• The IIHHO algorithm shows significant promise for numerical optimization tasks and real-world engineering applications.
• The enhancements contribute to a more robust and accurate optimization process.