{"url":"/method/gbo-1","slug":"gbo-1","name":"GBO","full_name":"Gradient-based optimization","full_name_withheld":false,"description_markdown":"GBO is a novel metaheuristic optimization algorithm. The GBO, inspired by the gradient-based Newton’s method, uses two main operators: gradient search rule (GSR) and local escaping operator (LEO) and a set of vectors to explore the search space. The GSR employs the gradient-based method to enhance the exploration tendency and accelerate the convergence rate to achieve better positions in the search space. The LEO enables the proposed GBO to escape from local optima. The performance of the new algorithm was evaluated in two phases. 28 mathematical test functions were first used to evaluate various characteristics of the GBO, and then six engineering problems were optimized by the GBO. In the first phase, the GBO was compared with five existing optimization algorithms, indicating that the GBO yielded very promising results due to its enhanced capabilities of exploration, exploitation, convergence, and effective avoidance of local optima. The second phase also demonstrated the superior performance of the GBO in solving complex real-world engineering problems. \r\n\r\n* The source codes of GBO are publicly available at https://imanahmadianfar.com/codes/.","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://imanahmadianfar.com/codes/","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":6,"archive_num_papers":6,"papers_newest_first":[{"paper":null,"title":"A Joint Python/C++ Library for Efficient yet Accessible Black-Box and Gray-Box Optimization with GOMEA","date":"2023-05-10","arxiv_id":"2305.06246","n_code_links":0,"syntology":null},{"paper":null,"title":"GBO:AMulti-Granularity Optimization Algorithm via Granular-ball for Continuous Problems","date":"2023-03-18","arxiv_id":"2303.12807","n_code_links":0,"syntology":null},{"paper":null,"title":"Imbalanced Class Data Performance Evaluation and Improvement using Novel Generative Adversarial Network-based Approach: SSG and GBO","date":"2022-10-23","arxiv_id":"2210.12870","n_code_links":0,"syntology":null},{"paper":null,"title":"GPU-Accelerated Parallel Gene-pool Optimal Mixing in a Gray-Box Optimization Setting","date":"2022-03-16","arxiv_id":"2203.08680","n_code_links":0,"syntology":null},{"paper":"/paper/risk-aware-scene-sampling-for-dynamic","title":"Risk-Aware Scene Sampling for Dynamic Assurance of Autonomous Systems","date":"2022-02-28","arxiv_id":"2202.13510","n_code_links":1,"syntology":null},{"paper":null,"title":"Gradient-based Bit Encoding Optimization for Noise-Robust Binary Memristive Crossbar","date":"2022-01-05","arxiv_id":"2201.01479","n_code_links":0,"syntology":null}],"papers_shown":6,"tasks":[{"task":"/task/bayesian-optimization","name":"Bayesian Optimization","papers":1},{"task":"/task/evolutionary-algorithms","name":"Evolutionary Algorithms","papers":1},{"task":null,"name":"GPU","papers":1},{"task":null,"name":"Generative Adversarial Network","papers":1},{"task":"/task/scene-generation","name":"Scene Generation","papers":1}],"tasks_shown":5,"n_tasks":5,"usage_by_year":[{"year":"2022","papers":4},{"year":"2023","papers":2}],"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/gbo-1"},"syntology_read_at":"2026-09-24T18:15:14+00:00"}