Knowledge-Based Systems, Год журнала: 2023, Номер 275, С. 110679 - 110679
Опубликована: Июнь 5, 2023
Язык: Английский
Knowledge-Based Systems, Год журнала: 2022, Номер 259, С. 110011 - 110011
Опубликована: Окт. 28, 2022
In this paper, a new metaheuristic algorithm called the Coati Optimization Algorithm (COA) is introduced, which mimics coati behavior in nature. The fundamental idea of COA simulation two natural behaviors coatis: (i) their when attacking and hunting iguanas (ii) escape from predators. implementation steps are described mathematically modeled phases exploration exploitation. performance evaluated on fifty-one objective functions, including twenty-nine functions IEEE CEC-2017 test suite twenty-two real-world applications CEC-2011 suite. COA's results compared to those eleven well-known algorithms. indicate that has an evident superiority over algorithms by balancing global search exploitation local search, far more competitive. To assess effectiveness applications, proposed approach implemented four practical optimization problems, high capability dealing with these types problems.
Язык: Английский
Процитировано
523
Engineering Applications of Artificial Intelligence, Год журнала: 2022, Номер 114, С. 105075 - 105075
Опубликована: Июль 16, 2022
Язык: Английский
Процитировано
306
Knowledge-Based Systems, Год журнала: 2023, Номер 268, С. 110454 - 110454
Опубликована: Март 11, 2023
Язык: Английский
Процитировано
290
Artificial Intelligence Review, Год журнала: 2023, Номер 56(S2), С. 1919 - 1979
Опубликована: Сен. 2, 2023
Язык: Английский
Процитировано
261
Knowledge-Based Systems, Год журнала: 2022, Номер 260, С. 110146 - 110146
Опубликована: Ноя. 29, 2022
Язык: Английский
Процитировано
172
Advanced Engineering Informatics, Год журнала: 2023, Номер 58, С. 102210 - 102210
Опубликована: Окт. 1, 2023
Язык: Английский
Процитировано
161
Scientific Reports, Год журнала: 2024, Номер 14(1)
Опубликована: Фев. 29, 2024
Abstract The novelty of this article lies in introducing a novel stochastic technique named the Hippopotamus Optimization (HO) algorithm. HO is conceived by drawing inspiration from inherent behaviors observed hippopotamuses, showcasing an innovative approach metaheuristic methodology. conceptually defined using trinary-phase model that incorporates their position updating rivers or ponds, defensive strategies against predators, and evasion methods, which are mathematically formulated. It attained top rank 115 out 161 benchmark functions finding optimal value, encompassing unimodal high-dimensional multimodal functions, fixed-dimensional as well CEC 2019 test suite 2014 dimensions 10, 30, 50, 100 Zigzag Pattern suggests demonstrates noteworthy proficiency both exploitation exploration. Moreover, it effectively balances exploration exploitation, supporting search process. In light results addressing four distinct engineering design challenges, has achieved most efficient resolution while concurrently upholding adherence to designated constraints. performance evaluation algorithm encompasses various aspects, including comparison with WOA, GWO, SSA, PSO, SCA, FA, GOA, TLBO, MFO, IWO recognized extensively researched metaheuristics, AOA recently developed algorithms, CMA-ES high-performance optimizers acknowledged for success IEEE competition. According statistical post hoc analysis, determined be significantly superior investigated algorithms. source codes publicly available at https://www.mathworks.com/matlabcentral/fileexchange/160088-hippopotamus-optimization-algorithm-ho .
Язык: Английский
Процитировано
154
Knowledge-Based Systems, Год журнала: 2023, Номер 284, С. 111257 - 111257
Опубликована: Дек. 22, 2023
Язык: Английский
Процитировано
146
Artificial Intelligence Review, Год журнала: 2023, Номер 56(9), С. 9329 - 9400
Опубликована: Янв. 30, 2023
Язык: Английский
Процитировано
120
Knowledge-Based Systems, Год журнала: 2022, Номер 261, С. 110206 - 110206
Опубликована: Дек. 17, 2022
Язык: Английский
Процитировано
117