Cited by Aquila optimizer: review, results and applications

Modified crayfish optimization algorithm for solving multiple engineering application problems DOI

Heming Jia,

Xuelian Zhou,

Jinrui Zhang

et al.

Artificial Intelligence Review, Journal Year: 2024, Volume and Issue: 57(5)

Published: April 24, 2024

Abstract Crayfish Optimization Algorithm (COA) is innovative and easy to implement, but the crayfish search efficiency decreases in later stage of algorithm, algorithm fall into local optimum. To solve these problems, this paper proposes an modified optimization (MCOA). Based on survival habits crayfish, MCOA environmental renewal mechanism that uses water quality factors guide seek a better environment. In addition, integrating learning strategy based ghost antagonism enhances its ability evade optimality. evaluate performance MCOA, tests were performed using IEEE CEC2020 benchmark function experiments conducted four constraint engineering problems feature selection problems. For constrained improved by 11.16%, 1.46%, 0.08% 0.24%, respectively, compared with COA. average fitness value accuracy are 55.23% 10.85%, respectively. shows solving complex spatial practical application The combination environment updating significantly improves MCOA. This discovery has important implications for development field optimization. Graphical

Language: Английский

Citations

IEGQO-AOA: Information-Exchanged Gaussian Arithmetic Optimization Algorithm with Quasi-opposition learning DOI

Emre Çelik

Knowledge-Based Systems, Journal Year: 2022, Volume and Issue: 260, P. 110169 - 110169

Published: Nov. 30, 2022

Language: Английский

Citations

IHAOAVOA: An improved hybrid aquila optimizer and African vultures optimization algorithm for global optimization problems DOI

Yaning Xiao, Yanling Guo, Hao Cui

et al.

Mathematical Biosciences & Engineering, Journal Year: 2022, Volume and Issue: 19(11), P. 10963 - 11017

Published: Jan. 1, 2022

<abstract><p>Aquila Optimizer (AO) and African Vultures Optimization Algorithm (AVOA) are two newly developed meta-heuristic algorithms that simulate several intelligent hunting behaviors of Aquila vulture in nature, respectively. AO has powerful global exploration capability, whereas its local exploitation phase is not stable enough. On the other hand, AVOA possesses promising capability but insufficient mechanisms. Based on characteristics both algorithms, this paper, we propose an improved hybrid optimizer called IHAOAVOA to overcome deficiencies single algorithm provide higher-quality solutions for solving optimization problems. First, combined retain valuable search competence each. Then, a new composite opposition-based learning (COBL) designed increase population diversity help escape from optima. In addition, more effectively guide process balance exploitation, fitness-distance (FDB) selection strategy introduced modify core position update formula. The performance proposed comprehensively investigated analyzed by comparing against basic AO, AVOA, six state-of-the-art 23 classical benchmark functions IEEE CEC2019 test suite. Experimental results demonstrate achieves superior solution accuracy, convergence speed, optima avoidance than comparison methods most functions. Furthermore, practicality highlighted five engineering design Our findings reveal technique also highly competitive when addressing real-world tasks. source code publicly available at <a href="https://doi.org/10.24433/CO.2373662.v1" target="_blank">https://doi.org/10.24433/CO.2373662.v1</a>.</p></abstract>

Language: Английский

Citations

A reinforcement learning-based hybrid Aquila Optimizer and improved Arithmetic Optimization Algorithm for global optimization DOI

Haiyang Liu, Xingong Zhang, Hanxiao Zhang

et al.

Expert Systems with Applications, Journal Year: 2023, Volume and Issue: 224, P. 119898 - 119898

Published: March 21, 2023

Language: Английский

Citations

A Comprehensive Survey on Aquila Optimizer DOI

Buddhadev Sasmal, Abdelazim G. Hussien, Arunita Das

et al.

Archives of Computational Methods in Engineering, Journal Year: 2023, Volume and Issue: 30(7), P. 4449 - 4476

Published: June 7, 2023

Language: Английский

Citations

A Comprehensive Survey on Arithmetic Optimization Algorithm DOI

Krishna Gopal Dhal, Buddhadev Sasmal, Arunita Das

et al.

Archives of Computational Methods in Engineering, Journal Year: 2023, Volume and Issue: 30(5), P. 3379 - 3404

Published: March 15, 2023

Language: Английский

Citations

Modified reptile search algorithm with multi-hunting coordination strategy for global optimization problems DOI

Di Wu, Changsheng Wen, Honghua Rao

et al.

Mathematical Biosciences & Engineering, Journal Year: 2023, Volume and Issue: 20(6), P. 10090 - 10134

Published: Jan. 1, 2023

The reptile search algorithm (RSA) is a bionic proposed by Abualigah. et al. in 2020. RSA simulates the whole process of crocodiles encircling and catching prey. Specifically, stage includes high walking belly walking, hunting coordination cooperation. However, middle later stages iteration, most agents will move towards optimal solution. if solution falls into local optimum, population fall stagnation. Therefore, cannot converge when solving complex problems. To enable to solve more problems, this paper proposes multi-hunting strategy combining Lagrange interpolation teaching-learning-based optimization (TLBO) algorithm's student stage. Multi-hunting cooperation make multiple coordinate with each other. Compared original RSA, has been greatly improved RSA's global capability. Moreover, considering weak ability jump out optimum stages, adds Lens pposition-based learning (LOBL) restart strategy. Based on above strategy, modified (MRSA) proposed. verify strategies' effectiveness for 23 benchmark CEC2020 functions were used test MRSA's performance. In addition, solutions six engineering problems reflected applicability. It can be seen from experiment that MRSA better performance

Language: Английский

Citations

Self-adaptive hybrid mutation slime mould algorithm: Case studies on UAV path planning, engineering problems, photovoltaic models and infinite impulse response DOI

Yujun Zhang, Yufei Wang,

Yuxin Yan

et al.

Alexandria Engineering Journal, Journal Year: 2024, Volume and Issue: 98, P. 364 - 389

Published: May 11, 2024

There are many classic highly complex optimization problems in the world, therefore, it is still necessary to find an applicable and effective algorithm solve these problems. In this paper, self-adaptive hybrid cross mutation slime mold proposed, which AHCSMA, efficiently. Specifically, there three innovations paper: (i) new Cauchy operator developed improve ability of population; (ii) crossover rate balance mechanism proposed make up for neglected relationship between individuals rates. Then differential vector information dominant individual other population utilized increase evolution speed algorithm; (iii) restart opposition learning designed alleviate situation where falls into local optimality. To verify competitive UAV path planning problems, engineering nonlinear parameter extraction photovoltaic model identification infinite impulse response used test accumulation more than 50 algorithms as comparison algorithms, results report that AHCSMA extremely performs better when optimizing real-life

Language: Английский

Citations

Fast random opposition-based learning Aquila optimization algorithm DOI

S. Gopi,

Prabhujit Mohapatra

Heliyon, Journal Year: 2024, Volume and Issue: 10(4), P. e26187 - e26187

Published: Feb. 1, 2024

Meta-heuristic algorithms are usually employed to address a variety of challenging optimization problems. In recent years, there has been continuous effort develop new and efficient meta-heuristic algorithms. The Aquila Optimization (AO) algorithm is newly established swarm-based method that mimics the hunting strategy birds in nature. However, complex problems, AO shown sluggish convergence rate gets stuck local optimal region throughout process. To overcome this problem, study, mechanism named Fast Random Opposition-Based Learning (FROBL) combined with improve proposed approach called FROBLAO algorithm. validate performance algorithm, CEC 2005, 2019, 2020 test functions, along six real-life engineering tested. Moreover, statistical analyses such as Wilcoxon rank-sum test, t-test, Friedman performed analyze significant difference between other results demonstrate achieved outstanding effectiveness solving an extensive

Language: Английский

Citations

A feature selection method based on the Golden Jackal-Grey Wolf Hybrid Optimization Algorithm DOI

Guangwei Liu, Zhiqing Guo, Wei Liu

et al.

PLoS ONE, Journal Year: 2024, Volume and Issue: 19(1), P. e0295579 - e0295579

Published: Jan. 2, 2024

This paper proposes a feature selection method based on hybrid optimization algorithm that combines the Golden Jackal Optimization (GJO) and Grey Wolf Optimizer (GWO). The primary objective of this is to create an effective data dimensionality reduction technique for eliminating redundant, irrelevant, noisy features within high-dimensional datasets. Drawing inspiration from Chinese idiom “Chai Lang Hu Bao,” mechanisms, cooperative behaviors observed in natural animal populations, we amalgamate GWO algorithm, Lagrange interpolation method, GJO propose multi-strategy fusion GJO-GWO algorithm. In Case 1, addressed eight complex benchmark functions. 2, was utilized tackle ten problems. Experimental results consistently demonstrate under identical experimental conditions, whether solving functions or addressing problems, exhibits smaller means, lower standard deviations, higher classification accuracy, reduced execution times. These findings affirm superior performance, stability

Language: Английский

Citations