Feature selections based on fuzzy probability dominance rough sets in interval-valued ordered decision systems DOI
Xia Liu, Xianyong Zhang, Benwei Chen

et al.

International Journal of Machine Learning and Cybernetics, Journal Year: 2025, Volume and Issue: unknown

Published: Feb. 28, 2025

Language: Английский

Liver Cancer Algorithm: A novel bio-inspired optimizer DOI
Essam H. Houssein, Diego Oliva, Nagwan Abdel Samee

et al.

Computers in Biology and Medicine, Journal Year: 2023, Volume and Issue: 165, P. 107389 - 107389

Published: Aug. 30, 2023

Language: Английский

Citations

149
Electric eel foraging optimization: A new bio-inspired optimizer for engineering applications DOI
Weiguo Zhao, Liying Wang, Zhenxing Zhang

et al.

Expert Systems with Applications, Journal Year: 2023, Volume and Issue: 238, P. 122200 - 122200

Published: Oct. 23, 2023

Language: Английский

Citations

125
A modified reptile search algorithm for global optimization and image segmentation: Case study brain MRI images DOI

Marwa M. Emam,

Essam H. Houssein,

Rania M. Ghoniem

et al.

Computers in Biology and Medicine, Journal Year: 2022, Volume and Issue: 152, P. 106404 - 106404

Published: Dec. 6, 2022

Language: Английский

Citations

76
MMKE: Multi-trial vector-based monkey king evolution algorithm and its applications for engineering optimization problems DOI Creative Commons
Mohammad H. Nadimi-Shahraki, Shokooh Taghian, Hoda Zamani

et al.

PLoS ONE, Journal Year: 2023, Volume and Issue: 18(1), P. e0280006 - e0280006

Published: Jan. 3, 2023

Monkey king evolution (MKE) is a population-based differential evolutionary algorithm in which the single strategy and control parameter affect convergence balance between exploration exploitation. Since strategies have considerable impact on performance of algorithms, collaborating multiple can significantly enhance abilities algorithms. This our motivation to propose multi-trial vector-based monkey named MMKE. It introduces novel best-history trial vector producer (BTVP) random (RTVP) that effectively collaborate with canonical MKE (MKE-TVP) using approach tackle various real-world optimization problems diverse challenges. expected proposed MMKE improve global search capability, strike exploitation, prevent original from converging prematurely during process. The was assessed CEC 2018 test functions, results were compared eight metaheuristic As result experiments, it demonstrated capable producing competitive superior terms accuracy rate comparison comparative Additionally, Friedman used examine gained experimental statistically, proving Furthermore, four engineering design optimal power flow (OPF) problem for IEEE 30-bus system are optimized demonstrate MMKE's real applicability. showed handle difficulties associated able solve multi-objective OPF better solutions than

Language: Английский

Citations

42
Artemisinin optimization based on malaria therapy: Algorithm and applications to medical image segmentation DOI

Yuan Chong,

Dong Zhao, Ali Asghar Heidari

et al.

Displays, Journal Year: 2024, Volume and Issue: 84, P. 102740 - 102740

Published: May 4, 2024

Language: Английский

Citations

40
Chaotic RIME optimization algorithm with adaptive mutualism for feature selection problems DOI
Mahmoud Abdel-Salam,

Gang Hu,

Emre Çelik

et al.

Computers in Biology and Medicine, Journal Year: 2024, Volume and Issue: 179, P. 108803 - 108803

Published: July 1, 2024

Language: Английский

Citations

40
GOOSE algorithm: a powerful optimization tool for real-world engineering challenges and beyond DOI
Rebwar Khalid Hamad, Tarik A. Rashid

Evolving Systems, Journal Year: 2024, Volume and Issue: 15(4), P. 1249 - 1274

Published: Jan. 11, 2024

Language: Английский

Citations

36
Hybrid beluga whale optimization algorithm with multi-strategy for functions and engineering optimization problems DOI Creative Commons

Jiaxu Huang,

Haiqing Hu

Journal Of Big Data, Journal Year: 2024, Volume and Issue: 11(1)

Published: Jan. 2, 2024

Abstract Beluga Whale Optimization (BWO) is a new metaheuristic algorithm that simulates the social behaviors of beluga whales swimming, foraging, and whale falling. Compared with other optimization algorithms, BWO shows certain advantages in solving unimodal multimodal problems. However, convergence speed performance still have some deficiencies when complex multidimensional Therefore, this paper proposes hybrid method called HBWO combining Quasi-oppositional based learning (QOBL), adaptive spiral predation strategy, Nelder-Mead simplex search (NM). Firstly, initialization phase, QOBL strategy introduced. This reconstructs initial spatial position population by pairwise comparisons to obtain more prosperous higher quality population. Subsequently, an designed exploration exploitation phases. The first learns optimal individual positions dimensions through avoid loss local optimality. At same time, movement motivated cosine factor introduced maintain balance between exploitation. Finally, NM added. It corrects multiple scaling methods improve accurately efficiently. verified utilizing CEC2017 CEC2019 test functions. Meanwhile, superiority six engineering design examples. experimental results show has feasibility effectiveness practical problems than methods.

Language: Английский

Citations

24
A Halton Enhanced Solution-based Human Evolutionary Algorithm for Complex Optimization and Advanced Feature Selection Problems DOI
Mahmoud Abdel-Salam, Amit Chhabra, Malik Braik

et al.

Knowledge-Based Systems, Journal Year: 2025, Volume and Issue: unknown, P. 113062 - 113062

Published: Jan. 1, 2025

Language: Английский

Citations

2
Optimized deep learning architecture for brain tumor classification using improved Hunger Games Search Algorithm DOI

Marwa M. Emam,

Nagwan Abdel Samee, Mona Jamjoom

et al.

Computers in Biology and Medicine, Journal Year: 2023, Volume and Issue: 160, P. 106966 - 106966

Published: April 24, 2023

Language: Английский

Citations

40