article · Neural Computing and Applications
This research introduces an improved Harris Hawks optimisation (IHHO) algorithm designed to overcome the common issue of the original Harris Hawks optimisation (HHO) algorithm getting trapped in local minima. The IHHO algorithm incorporates new strategies for both the exploration and exploitation phases, including random location-based habitats and specific chasing patterns, alongside logarithmic and exponential functions to explore new regions more effectively. The algorithm's performance was rigorously evaluated against five other recent metaheuristic algorithms and three modified HHO versions across various standard benchmarks, including CEC2017, CEC2019, CEC2020, and 52 other functions. Additionally, IHHO was applied to six classical real-world engineering problems, demonstrating its superior efficiency.
Optimisation algorithms are crucial for finding the best solutions to complex problems across many fields. An algorithm that avoids local minima and consistently finds better global solutions, like IHHO, can lead to more efficient designs, improved processes, and better outcomes in various engineering and scientific applications.
This research presents an improved optimisation algorithm that has been tested on classical real-world engineering problems. It could be applied in various engineering domains, such as design optimisation, resource allocation, or scheduling, to find more efficient and effective solutions. Potential users include engineers, data scientists, and researchers working on complex system design and operational challenges. This appears to be applied research, with the algorithm tested on practical problems, suggesting it is moving towards real-world implementation.
AI-generated from the published abstract. Always read the original work before citing.
Abstract Harris Hawks optimization (HHO) algorithm was a powerful metaheuristic algorithm for solving complex problems. However, HHO could easily fall within the local minimum. In this paper, we proposed an improved Harris Hawks optimization (IHHO) algorithm for solving different engineering tasks. The proposed algorithm focused on random location-based habitats during the exploration phase and on strategies 1, 3, and 4 during the exploitation phase. The proposed modified Harris hawks in the wild would change their perch strategy and chasing pattern according to updates in both the exploration and exploitation phases. To avoid being stuck in a local solution, random values were generated using logarithms and exponentials to explore new regions more quickly and locations. To evaluate the performance of the proposed algorithm, IHHO was compared to other five recent algorithms [grey wolf optimization, BAT algorithm, teaching–learning-based optimization, moth-flame optimization, and whale optimization algorithm] as well as three other modifications of HHO (BHHO, LogHHO, and MHHO). These optimizers had been applied to different benchmarks, namely standard benchmarks, CEC2017, CEC2019, CEC2020, and other 52 standard benchmark functions. Moreover, six classical real-world engineering problems were tested against the IHHO to prove the efficiency of the proposed algorithm. The numerical results showed the superiority of the proposed algorithm IHHO against other algorithms, which was proved visually using different convergence curves. Friedman's mean rank statistical test was also inducted to calculate the rank of IHHO against other algorithms. The results of the Friedman test indicated that the proposed algorithm was ranked first as compared to the other algorithms as well as three other modifications of HHO.
This page summarises published work. The authoritative version sits with the publisher.
DOI: 10.1007/s00521-024-09603-3
Is something wrong with this record? Report it or request removal.
Discussion
Have you built on this work, tried to replicate it, or seen it applied in practice? Share what you know. Verified researchers and MARATTO™ domain experts can open a discussion, and any member can reply. Contributions are reviewed before they appear.
No discussion yet. Open the first thread.
New to MARATTO™? Create a free account.
