{"url":"/method/hho","slug":"hho","name":"Harris Hawks optimization (HHO)","full_name":"Harris Hawks optimization","full_name_withheld":false,"description_markdown":"[HHO](https://aliasgharheidari.com/HHO.html) is a popular swarm-based, gradient-free optimization algorithm with several active and time-varying phases of exploration and exploitation. This algorithm initially published by the prestigious Journal of Future Generation Computer Systems (FGCS) in 2019, and from the first day, it has gained increasing attention among researchers due to its flexible structure, high performance, and high-quality results. The main logic of the HHO method is designed based on the cooperative behaviour and chasing styles of Harris' hawks in nature called \"surprise pounce\". Currently, there are many suggestions about how to enhance the functionality of HHO, and there are also several enhanced variants of the HHO in the leading Elsevier and IEEE transaction journals.\r\n\r\nFrom the algorithmic behaviour viewpoint, there are several effective features in HHO :\r\nEscaping energy parameter has a dynamic randomized time-varying nature, which can further improve and harmonize the exploratory and exploitive patterns of HHO. This factor also supports HHO to conduct a smooth transition between exploration and exploitation.\r\nDifferent exploration mechanisms with respect to the average location of hawks can increase the exploratory trends of HHO throughout initial iterations.\r\nDiverse LF-based patterns with short-length jumps enrich the exploitative behaviours of HHO when directing a local search.\r\nThe progressive selection scheme supports search agents to progressively advance their position and only select a better position, which can improve the superiority of solutions and intensification powers of HHO throughout the optimization procedure.\r\nHHO shows a series of searching strategies and then, it selects the best movement step. This feature has also a constructive influence on the exploitation inclinations of HHO.\r\nThe randomized jump strength can assist candidate solutions in harmonising the exploration and exploitation leanings.\r\nThe application of adaptive and time-varying components allows HHO to handle difficulties of a feature space including local optimal solutions, multi-modality, and deceptive optima.\r\n\r\n🔗 The source codes of HHO are publicly available at https://aliasgharheidari.com/HHO.html","description_state":"present","introduced_year":null,"introduced_by":{"title":null,"paper":null,"first_author":null,"n_authors":0,"url_abs":null,"archive_paper_url":null},"source":{"url":null,"title":null,"url_on_a_paper_host":false},"code_snippet_url":"https://aliasgharheidari.com/HHO.html","code_snippet_url_on_a_code_host":false,"categories":[{"area":"General","area_id":"general","collection":"Optimization","url":"/methods/category/optimization","pwc_aliases":[]}],"n_papers_tagged":5,"archive_num_papers":5,"papers_newest_first":[{"paper":null,"title":"Diagnosis of Malignant Lymphoma Cancer Using Hybrid Optimized Techniques Based on Dense Neural Networks","date":"2024-10-09","arxiv_id":"2410.06974","n_code_links":0,"syntology":null},{"paper":null,"title":"Electric Vehicles coordination for grid balancing using multi-objective Harris Hawks Optimization","date":"2023-11-24","arxiv_id":"2311.14563","n_code_links":0,"syntology":null},{"paper":null,"title":"Harris Hawks Feature Selection in Distributed Machine Learning for Secure IoT Environments","date":"2023-02-20","arxiv_id":"2302.12205","n_code_links":0,"syntology":null},{"paper":"/paper/segmentation-of-brain-mri-using-an-altruistic","title":"Segmentation of Brain MRI using an Altruistic Harris Hawks' Optimization algorithm","date":"2021-09-17","arxiv_id":"2109.08688","n_code_links":1,"syntology":null},{"paper":null,"title":"Sum-Rate Maximization for UAV-assisted Visible Light Communications using NOMA: Swarm Intelligence meets Machine Learning","date":"2021-01-10","arxiv_id":"2101.03498","n_code_links":0,"syntology":null}],"papers_shown":5,"tasks":[{"task":"/task/machine-learning","name":"BIG-bench Machine Learning","papers":1},{"task":"/task/diagnostic","name":"Diagnostic","papers":1},{"task":"/task/segmentation","name":"Segmentation","papers":1},{"task":"/task/feature-selection","name":"feature selection","papers":1}],"tasks_shown":4,"n_tasks":4,"usage_by_year":[{"year":"2021","papers":2},{"year":"2023","papers":2},{"year":"2024","papers":1}],"row_source":"methods_table","archive":{"source":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","archive_url":"https://paperswithcode.com/method/hho"},"syntology_read_at":"2026-09-24T18:15:14+00:00"}