Applied Soft Computing, Год журнала: 2021, Номер 111, С. 107698 - 107698
Опубликована: Июль 14, 2021
Язык: Английский
Mathematics, Год журнала: 2022, Номер 10(10), С. 1626 - 1626
Опубликована: Май 10, 2022
This paper presents a novel metaheuristic optimization algorithm inspired by the geometrical features of circles, called circle search (CSA). The is most well-known geometric object, with various including diameter, center, perimeter, and tangent lines. ratio between radius line segment orthogonal function angle opposite to radius. plays an important role in exploration exploitation behavior CSA. To evaluate robustness CSA comparison other algorithms, many independent experiments employing 23 famous functions 3 real engineering problems were carried out. statistical results revealed that succeeded achieving minimum fitness values for 21 out tested functions, p-value was less than 0.05. evidence converged faster comparative algorithms. Furthermore, high-dimensional used assess CSA’s robustness, revealing robust problems. As result, proposed promising can be easily handle wide range
Язык: Английский
Процитировано
77
Engineering Applications of Artificial Intelligence, Год журнала: 2022, Номер 117, С. 105622 - 105622
Опубликована: Ноя. 25, 2022
The aim of this study was to gather, discuss, and compare recently developed metaheuristics understand the pace development in field make some recommendations for research community practitioners. By thoroughly comprehensively searching literature narrowing search results, we created with a list 57 novel metaheuristic algorithms. Based on availability source code, reviewed analysed optimization capability 26 these algorithms through series experiments. We also evaluated exploitation exploration capabilities by using 50 unimodal functions multimodal functions, respectively. In addition, assessed balance 29 shifted, rotated, composite, hybrid CEC-BC-2017 benchmark functions. Moreover, applicability four real-world constrained engineering problems. To rank algorithms, performed nonparametric statistical test, Friedman mean test. results declared that GBO, PO, MRFO have better capabilities. found MPA, FBI, HBO be most balanced. Finally, based problems, HBO, MA are suitable. Collectively, confidently recommend
Язык: Английский
Процитировано
73
Expert Systems with Applications, Год журнала: 2023, Номер 232, С. 120886 - 120886
Опубликована: Июнь 24, 2023
Язык: Английский
Процитировано
54
Neural Computing and Applications, Год журнала: 2023, Номер unknown
Опубликована: Янв. 7, 2023
Abstract Particle swarm optimization (PSO) is one of the most well-regard metaheuristics with remarkable performance when solving diverse problems. However, PSO faces two main problems that degrade its performance: slow convergence and local optima entrapment. In addition, this algorithm substantially degrades on high-dimensional classical PSO, particles can move in each iteration either slower or faster speed. This work proposes a novel idea called velocity pausing where proposed (VPPSO) variant are supported by third movement option allows them to same as they did previous iteration. As result, VPPSO has higher potential balance exploration exploitation. To avoid premature convergence, modifies first term equation. population divided into swarms maintain diversity. The validated forty three benchmark functions four real-world engineering According Wilcoxon rank-sum Friedman tests, significantly outperform seven prominent algorithms tested both low- cases. Due superior complex problems, be applied solve Moreover, concept easily integrated new existing metaheuristic enhance their performances. Matlab code available at: https://uk.mathworks.com/matlabcentral/fileexchange/119633-vppso .
Язык: Английский
Процитировано
47
Applied Soft Computing, Год журнала: 2023, Номер 145, С. 110573 - 110573
Опубликована: Июль 7, 2023
Язык: Английский
Процитировано
45
The Journal of Supercomputing, Год журнала: 2024, Номер 80(15), С. 22913 - 23017
Опубликована: Июль 1, 2024
Язык: Английский
Процитировано
39
Biomimetics, Год журнала: 2024, Номер 9(2), С. 65 - 65
Опубликована: Янв. 23, 2024
A new bio-inspired metaheuristic algorithm named the Pufferfish Optimization Algorithm (POA), that imitates natural behavior of pufferfish in nature, is introduced this paper. The fundamental inspiration POA adapted from defense mechanism against predators. In mechanism, by filling its elastic stomach with water, becomes a spherical ball pointed spines, and as result, hungry predator escapes threat. theory stated then mathematically modeled two phases: (i) exploration based on simulation predator’s attack (ii) exploitation escape spiny pufferfish. performance evaluated handling CEC 2017 test suite for problem dimensions equal to 10, 30, 50, 100. optimization results show has achieved an effective solution appropriate ability exploration, exploitation, balance between them during search process. quality process compared twelve well-known algorithms. provides superior achieving better most benchmark functions order solve competitor Also, effectiveness handle tasks real-world applications twenty-two constrained problems 2011 four engineering design problems. Simulation solutions
Язык: Английский
Процитировано
34
Swarm and Evolutionary Computation, Год журнала: 2024, Номер 86, С. 101518 - 101518
Опубликована: Фев. 24, 2024
Язык: Английский
Процитировано
19
Artificial Intelligence Review, Год журнала: 2025, Номер 58(3)
Опубликована: Янв. 6, 2025
The advent of the intelligent information era has witnessed a proliferation complex optimization problems across various disciplines. Although existing meta-heuristic algorithms have demonstrated efficacy in many scenarios, they still struggle with certain challenges such as premature convergence, insufficient exploration, and lack robustness high-dimensional, nonconvex search spaces. These limitations underscore need for novel techniques that can better balance exploration exploitation while maintaining computational efficiency. In response to this need, we propose Artificial Lemming Algorithm (ALA), bio-inspired metaheuristic mathematically models four distinct behaviors lemmings nature: long-distance migration, digging holes, foraging, evading predators. Specifically, migration burrow are dedicated highly exploring domain, whereas foraging predators provide during process. addition, ALA incorporates an energy-decreasing mechanism enables dynamic adjustments between exploitation, thereby enhancing its ability evade local optima converge global solutions more robustly. To thoroughly verify effectiveness proposed method, is compared 17 other state-of-the-art on IEEE CEC2017 benchmark test suite CEC2022 suite. experimental results indicate reliable comprehensive performance achieve superior solution accuracy, convergence speed, stability most cases. For 29 10-, 30-, 50-, 100-dimensional functions, obtains lowest Friedman average ranking values among all competitor methods, which 1.7241, 2.1034, 2.7241, 2.9310, respectively, 12 again wins optimal 2.1667. Finally, further evaluate applicability, implemented address series cases, including constrained engineering design, photovoltaic (PV) model parameter identification, fractional-order proportional-differential-integral (FOPID) controller gain tuning. Our findings highlight competitive edge potential real-world applications. source code publicly available at https://github.com/StevenShaw98/Artificial-Lemming-Algorithm .
Язык: Английский
Процитировано
12
IEEE Access, Год журнала: 2020, Номер 8, С. 195929 - 195945
Опубликована: Янв. 1, 2020
Feature selection is a process to reduce the dimension of dataset by removing redundant features, and use optimal subset features for machine learning or data mining algorithms. This helps minimize time requirement train algorithm as well lessen storage ignoring less-informative features. can be considered combinatorial optimization problem. In this paper, authors have presented new feature called Mayfly-Harmony Search (MA-HS) based on two meta-heuristics namely Mayfly Algorithm Harmony Search. has not hitherto been used problems best author's knowledge. An S-shaped transfer function incorporated converting it into binary version Algorithm. When different candidate solutions obtained from various regions search space using are taken harmony memory processed Search, superior solution ensured. primary reason proposing hybrid Thus, combining with leads an increased exploitation overall improvement in performance algorithm. The proposed applied 18 UCI datasets compared 12 other state-of-the-art meta-heuristic FS methods. Experiments also performed three high-dimensional microarray datasets. results support source code found link follows: https://github.com/trin07/MA-HS.
Язык: Английский
Процитировано
113