Improved Artificial Bee Colony Using Sine-Cosine Algorithm for Multi-Level Thresholding Image Segmentation DOI Creative Commons
Ahmed A. Ewees, Mohamed Abd Elaziz, Mohammed A. A. Al‐qaness

и другие.

IEEE Access, Год журнала: 2020, Номер 8, С. 26304 - 26315

Опубликована: Янв. 1, 2020

Multilevel-thresholding is an efficient method used in image segmentation. This paper presents a hybrid meta-heuristic approach for multi-level thresholding segmentation by integrating both the artificial bee colony (ABC) algorithm and sine-cosine (SCA). The proposed algorithm, called ABCSCA, applied to segment images it utilizes Otsu's function as objective function. ABCSCA uses ABC optimize threshold reduce search region. Thereafter, SCA output of determine global optimal solution, which represents values. To evaluate performance set experimental series performed using nineteen images. In first series, assessed at low levels compared with traditional methods. Moreover, second aims high six algorithms addition ABC. Besides, evaluated fuzzy entropy. results demonstrate effectiveness showed that outperforms other terms measures, such Peak Signal-to-Noise Ratio (PSNR) Structural Similarity Index (SSIM).

Язык: Английский

A survey on swarm intelligence approaches to feature selection in data mining DOI
Bach Hoai Nguyen, Bing Xue, Mengjie Zhang

и другие.

Swarm and Evolutionary Computation, Год журнала: 2020, Номер 54, С. 100663 - 100663

Опубликована: Фев. 6, 2020

Язык: Английский

Процитировано

343

Improved Binary Grey Wolf Optimizer and Its application for feature selection DOI
Pei Hu, Jeng‐Shyang Pan, Shu‐Chuan Chu

и другие.

Knowledge-Based Systems, Год журнала: 2020, Номер 195, С. 105746 - 105746

Опубликована: Март 9, 2020

Язык: Английский

Процитировано

332

An exhaustive review of the metaheuristic algorithms for search and optimization: taxonomy, applications, and open challenges DOI Open Access
Kanchan Rajwar, Kusum Deep, Swagatam Das

и другие.

Artificial Intelligence Review, Год журнала: 2023, Номер 56(11), С. 13187 - 13257

Опубликована: Апрель 9, 2023

Язык: Английский

Процитировано

254

A wrapper-filter feature selection technique based on ant colony optimization DOI
Manosij Ghosh, Ritam Guha, Ram Sarkar

и другие.

Neural Computing and Applications, Год журнала: 2019, Номер 32(12), С. 7839 - 7857

Опубликована: Апрель 11, 2019

Язык: Английский

Процитировано

178

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

и другие.

Computers in Biology and Medicine, Год журнала: 2023, Номер 165, С. 107389 - 107389

Опубликована: Авг. 30, 2023

Язык: Английский

Процитировано

156

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

и другие.

Expert Systems with Applications, Год журнала: 2023, Номер 238, С. 122200 - 122200

Опубликована: Окт. 23, 2023

Язык: Английский

Процитировано

132

Advanced metaheuristic optimization techniques in applications of deep neural networks: a review DOI
Mohamed Abd Elaziz, Abdelghani Dahou, Laith Abualigah

и другие.

Neural Computing and Applications, Год журнала: 2021, Номер 33(21), С. 14079 - 14099

Опубликована: Апрель 18, 2021

Язык: Английский

Процитировано

129

Improved bald eagle search algorithm for global optimization and feature selection DOI Creative Commons
Amit Chhabra, Abdelazim G. Hussien, Fatma A. Hashim

и другие.

Alexandria Engineering Journal, Год журнала: 2023, Номер 68, С. 141 - 180

Опубликована: Янв. 18, 2023

The use of metaheuristics is one the most encouraging methodologies for taking care real-life problems. Bald eagle search (BES) algorithm latest swarm-intelligence metaheuristic inspired by intelligent hunting behavior bald eagles. In recent research works, BES has performed reasonably well over a wide range application areas such as chemical engineering, environmental science, physics and astronomy, structural modeling, global optimization, engineering design, energy efficiency, etc. However, it still lacks adequate searching efficiency tendency to stuck in local optima which affects final outcome. This paper introduces modified (mBES) that removes shortcomings original incorporating three improvements; Opposition-based learning (OBL), Chaotic Local Search (CLS), Transition & Pharsor operators. OBL embedded different phases standard viz. initial population, selecting, space, swooping update positions individual solutions strengthen exploration, CLS used enhance position best agent will lead enhancing all individuals, operators help provide sufficient exploration–exploitation trade-off. mBES initially evaluated with 29 CEC2017 10 CEC2020 optimization benchmark functions. addition, practicality tested real-world feature selection problem five design Results are compared against number classical algorithms using statistical metrics, convergence analysis, box plots, Wilcoxon rank sum test. case composite test functions F21-F30, wins 70% cases, whereas rest functions, generates good results 65% cases. proposed produces performance 55% 45% generated competitive results. On other hand, problems, among algorithms. problem, also showed competitiveness observations problems show superiority robustness baseline metaheuristics. It can be safely concluded improvements suggested proved effective making enough solve variety

Язык: Английский

Процитировано

67

SRL-ACO: A text augmentation framework based on semantic role labeling and ant colony optimization DOI Creative Commons
Aytuğ Onan

Journal of King Saud University - Computer and Information Sciences, Год журнала: 2023, Номер 35(7), С. 101611 - 101611

Опубликована: Июнь 10, 2023

The process of creating high-quality labeled data is crucial for training machine-learning models, but it can be a time-consuming and labor-intensive process. Moreover, manual annotation by human annotators lead to varying degrees competency, training, experience, which result in inconsistent labeling arbitrary standards. To address these challenges, researchers have been exploring automated methods enhancing testing datasets. This paper proposes SRL-ACO, novel text augmentation framework that leverages Semantic Role Labeling (SRL) Ant Colony Optimization (ACO) techniques generate additional natural language processing (NLP) models. uses SRL identify the semantic roles words sentence ACO new sentences preserve roles. SRL-ACO enhance accuracy NLP models generating without requiring annotation. presents experimental results demonstrating effectiveness on seven classification datasets sentiment analysis, toxic detection sarcasm identification. show improves performance classifier different tasks. These demonstrate has potential quality quantity various

Язык: Английский

Процитировано

60

Quadratic interpolation and a new local search approach to improve particle swarm optimization: Solar photovoltaic parameter estimation DOI
Mohammed Qaraad,

Souad Amjad,

Nazar K. Hussein

и другие.

Expert Systems with Applications, Год журнала: 2023, Номер 236, С. 121417 - 121417

Опубликована: Сен. 1, 2023

Язык: Английский

Процитировано

59