Article
Computer Science, Artificial Intelligence
Benyamin Abdollahzadeh, Farhad Soleimanian Gharehchopogh, Seyedali Mirjalili
Summary: Metaheuristics play a crucial role in solving optimization problems, often inspired by the collective intelligence of natural organisms. This paper introduces a new metaheuristic algorithm, GTO, inspired by gorilla troops' social intelligence in nature. Results show that the GTO outperforms existing metaheuristics on most benchmark functions and engineering problems, especially in high-dimensional scenarios.
INTERNATIONAL JOURNAL OF INTELLIGENT SYSTEMS
(2021)
Article
Computer Science, Artificial Intelligence
Laith Abualigah, Mohamed Abd Elaziz, Putra Sumari, Zong Woo Geem, Amir H. Gandomi
Summary: The paper introduces a novel nature-inspired meta-heuristic optimizer, RSA, based on the hunting behavior of crocodiles. Through implementing two main steps of crocodile behavior, RSA shows unique search methods compared to existing algorithms, and achieves better results in various test functions and engineering problems.
EXPERT SYSTEMS WITH APPLICATIONS
(2022)
Article
Computer Science, Artificial Intelligence
Shijie Zhao, Tianran Zhang, Shilin Ma, Mengchen Wang
Summary: This paper proposes a novel swarm intelligence-based metaheuristic called sea-horse optimizer (SHO) which mimics the movement, predation, and breeding behaviors of sea horses in nature. The algorithm is designed to balance local exploitation and global exploration, and has been shown to be a high-performance optimizer with positive adaptability to deal with constraint problems.
APPLIED INTELLIGENCE
(2023)
Article
Computer Science, Artificial Intelligence
Neetesh Kumar, Navjot Singh, Deo Prakash Vidyarthi
Summary: This study models the dynamic foraging behavior of Redheaded Agama lizards and proposes an artificial lizard search optimization (ALSO) algorithm based on their effective way of capturing prey. The simulation demonstrates the effectiveness of the proposed algorithm over other nature-inspired optimization techniques.
Article
Computer Science, Artificial Intelligence
Malik Braik, Abdelaziz Hammouri, Jaffar Atwan, Mohammed Azmi A. Al-Betar, Mohammed A. Awadallah
Summary: This paper introduces a novel meta-heuristic algorithm, White Shark Optimizer (WSO), inspired by the behaviors of great white sharks and mathematically modeled to achieve optimization in search spaces. Through comprehensive benchmarking and application to real-world problems, WSO demonstrates reliability and applicability in solving optimization problems.
KNOWLEDGE-BASED SYSTEMS
(2022)
Article
Computer Science, Interdisciplinary Applications
Benyamin Abdollahzadeh, Farhad Soleimanian Gharehchopogh, Nima Khodadadi, Seyedali Mirjalili
Summary: This paper proposes a novel meta-heuristic algorithm called the Mountain Gazelle Optimizer (MGO), which is inspired by the social life and hierarchy of wild mountain gazelles. The MGO algorithm formulates the hierarchical and social life of gazelles mathematically to develop an optimization algorithm. It is evaluated and tested using standard benchmark functions and engineering problems, and compared with other meta-heuristic algorithms to validate its effectiveness. The experiments show that the MGO performs better than the comparable algorithms and maintains good performance even when increasing problem dimensions.
ADVANCES IN ENGINEERING SOFTWARE
(2022)
Article
Computer Science, Artificial Intelligence
Gang Hu, Yuxuan Guo, Guo Wei, Laith Abualigah
Summary: This study presents a new nature-inspired metaheuristic algorithm called GKS optimizer (GKSO) based on the behavior of the Genghis Khan shark (GKS). The algorithm simulates the hunting, movement, foraging, and self-protection mechanisms of GKS to achieve efficient optimization in different regions of the search space. The qualitative and quantitative analysis confirms the exploration and exploitation capability of GKSO, and comparative experiments demonstrate its superiority over other algorithms.
ADVANCED ENGINEERING INFORMATICS
(2023)
Article
Computer Science, Artificial Intelligence
Gaurav Dhiman
Summary: The SSC algorithm combines sine-cosine functions and attacking strategy of SHO algorithm to find optimal solutions for complex problems, demonstrating robustness, effectiveness, efficiency, and convergence analysis in comparison with other competitor approaches.
KNOWLEDGE-BASED SYSTEMS
(2021)
Article
Computer Science, Information Systems
Olaide Nathaniel Oyelade, Absalom El-Shamir Ezugwu, Tehnan I. A. Mohamed, Laith Abualigah
Summary: This study proposes a novel bio-inspired and population-based optimization algorithm named Ebola Optimization Search Algorithm (EOSA) based on the propagation mechanism of the Ebola virus disease. The algorithm outperforms popular metaheuristic algorithms such as Particle Swarm Optimization Algorithm (PSO), Genetic Algorithm (GA), and Artificial Bee Colony Algorithm (ABC) in terms of scalability, convergence, and sensitivity analyses. The algorithm is also successfully applied to the problem of selecting the best combination of convolutional neural network (CNN) hyperparameters in the image classification of digital mammography.
Article
Computer Science, Artificial Intelligence
Farid MiarNaeimi, Gholamreza Azizyan, Mohsen Rashki
Summary: This paper introduces a new meta-heuristic algorithm called Horse Herd Optimization Algorithm (HOA), inspired by horses' behavior, which shows excellent performance in high-dimensional optimization problems. By imitating the behavior features of horses at different ages, HOA has a large number of control parameters leading to efficient solving of complex problems.
KNOWLEDGE-BASED SYSTEMS
(2021)
Article
Computer Science, Artificial Intelligence
Mohamed Abdel-Basset, Reda Mohamed, Mohammed Jameel, Mohamed Abouhawwash
Summary: This work presents a novel metaheuristic algorithm called Nutcracker Optimization Algorithm (NOA), inspired by the behaviors of Clark's nutcrackers. NOA mimics the nutcracker's search for seeds and cache storage during summer and fall, as well as its spatial memory strategy during winter and spring. The algorithm is evaluated and compared with other optimization algorithms, demonstrating superior results and ranking first among all methods.
KNOWLEDGE-BASED SYSTEMS
(2023)
Article
Computer Science, Artificial Intelligence
Laith Abualigah, Ali Diabat
Summary: This paper proposes a feature selection method called CGSO that combines chaotic maps and binary Group Search Optimizer. Experimental results demonstrate the superiority of this method over other published methods.
EXPERT SYSTEMS WITH APPLICATIONS
(2022)
Article
Computer Science, Information Systems
Mahdi Valikhan Anaraki, Saeed Farzin
Summary: This study presents a new natural-based algorithm called the Humboldt Squid Optimization Algorithm (HSOA), inspired by the hunting, moving, and mating behavior of Humboldt squids. HSOA addresses existing issues through processes such as attacking, escaping, successful attacks, larger squids attacking smaller ones, and mating. By connecting and cooperating, individuals in HSOA achieve optimal responses, making it versatile and applicable to mathematical and engineering problems. The study demonstrates that HSOA outperforms other algorithms in benchmark function problems and engineering problems.
Article
Computer Science, Interdisciplinary Applications
Amol M. Dalavi, Alyssa Gomes, Aaliya Javed Husain
Summary: This paper statistically evaluates the impact and importance of nature-inspired optimization by analyzing works published between 2016 and 2020. The study finds that China, India, and the US are the highest contributors, and computer science, engineering, and mathematics are the top disciplines contributing to research. The top application areas include optimization, artificial intelligence, and decision sciences.
COMPUTERS & INDUSTRIAL ENGINEERING
(2022)
Article
Computer Science, Interdisciplinary Applications
Iraj Naruei, Farshid Keynia
Summary: Nowadays, optimization algorithms inspired by the natural behavior of agents, such as humans, animals, or plants, have become popular in solving various scientific problems. The wild horse optimizer algorithm is inspired by the social behavior of wild horses, particularly their decency behavior where foals leave groups to prevent mating with relatives. The proposed algorithm has shown competitive results compared to other optimization methods in testing.
ENGINEERING WITH COMPUTERS
(2022)
Article
Computer Science, Interdisciplinary Applications
Laith Abualigah, Ali Diabat, Maryam Altalhi, Mohamed Abd Elaziz
Summary: This paper proposes an improved variant of Harris Hawks optimization (HHO) called HHSC to address the issues of slow convergence and falling into local optima trap in dealing with complex problems. Two search strategies using sine and cosine functions are added to enhance the convergence speed and exploration/exploitation searches of the algorithm. The experimental results demonstrate the promising performance of the proposed HHSC method in various benchmark functions and engineering design problems, outperforming other optimization methods.
ENGINEERING WITH COMPUTERS
(2023)
Article
Computer Science, Artificial Intelligence
Laith Abualigah, Ali Diabat
Summary: This paper proposes a new search method based on an augmented version of the Arithmetic Optimization Algorithm. By combining the Marine Predators Algorithm and Ensemble Mutation Strategy, the proposed method improves the performance of the Arithmetic Optimization Algorithm. Experimental results show that the method found new best solutions for complex problems and exhibits promising overall performance.
JOURNAL OF INTELLIGENT MANUFACTURING
(2023)
Article
Computer Science, Artificial Intelligence
Laith Abualigah, Ali Diabat, Davor Svetinovic, Mohamed Abd Elaziz
Summary: This paper presents a newly proposed metaheuristic algorithm, Harris Hawks Optimization (HHO), and its augmented modification called HHMV. By hybridizing with Multi-verse Optimizer, HHMV improves the convergence speed and search mechanisms of conventional HHO in multi-dimensional optimization problems. Experimental results show that HHMV outperforms other methods in terms of exploration and exploitation search mechanisms and convergence speed.
JOURNAL OF INTELLIGENT MANUFACTURING
(2023)
Article
Computer Science, Artificial Intelligence
Mohamed Abd Elaziz, Laith Abualigah, Ahmed A. Ewees, Mohammed A. A. Al-qaness, Reham R. Mostafa, Dalia Yousri, Rehab Ali Ibrahim
Summary: In this paper, a modified version of Manta Ray Foraging Optimization (MRFO) called MRTMO is proposed to overcome the issue of trapping in local solutions in metaheuristic techniques. The proposed MRTMO integrates the triangular mutation operator and orthogonal learning strategy to achieve a balance between algorithm cores and guide the search agents effectively. Extensive experiments demonstrate the competitive performance of MRTMO in solving optimization and engineering problems.
APPLIED INTELLIGENCE
(2023)
Article
Computer Science, Information Systems
Ala Mughaid, Shadi AlZu'bi, Asma Alnajjar, Esraa AbuElsoud, Subhieh El Salhi, Bashar Igried, Laith Abualigah
Summary: This paper introduces the Non Orthogonal Multiple Access (NOMA) technology in 5G communications and proposes a methodology for wireless cyberattack detection in 5G networks using various machine learning and deep learning techniques. The simulation and experiments show high accuracy rates for detecting dropping attacks and achieving outstanding performance with the KNN algorithm, Decision Forest, and Neural Network.
MULTIMEDIA TOOLS AND APPLICATIONS
(2023)
Correction
Computer Science, Information Systems
Ala Mughaid, Shadi AlZu'bi, Asma Alnajjar, Esraa AbuElsoud, Subhieh El Salhi, Bashar Igried, Laith Abualigah
MULTIMEDIA TOOLS AND APPLICATIONS
(2023)
Correction
Computer Science, Artificial Intelligence
Olatunji O. Akinola, Absalom E. Ezugwu, Jeffrey O. Agushaka, Raed Abu Zitar, Laith Abualigah
NEURAL COMPUTING & APPLICATIONS
(2023)
Article
Computer Science, Artificial Intelligence
Xuecong Zhang, Chen Zhong, Laith Abualigah
Summary: This paper proposes a DETS algorithm based on hybrid tabu search and differential evolution algorithms for optimizing foreign exchange portfolio allocation. It also introduces NSDE-TS algorithm for multi-objective optimization. The experiments demonstrate that both algorithms achieve excellent results in foreign exchange asset allocation.
Article
Computer Science, Artificial Intelligence
Hayfa Y. Abuaddous, Goldendeep Kaur, Kiran Jyoti, Nitin Mittal, Shubham Mahajan, Amit Kant Pandit, Anas Ratib Alsoud, Laith Abualigah
Summary: This article investigates the localization problem in wireless sensor networks and proposes an improved grey wolf optimization algorithm (R-GWO) for 3D environments. The R-GWO algorithm, which incorporates repulsion mechanism, outperforms the traditional GWO algorithm in terms of exploration and exploitation abilities. Experimental results show that R-GWO achieves the lowest localization error in 3D environments.
Article
Computer Science, Artificial Intelligence
Geetanjali Babbar, Rohit Bajaj, Nitin Mittal, Shubham Mahajan, Raed Abu Zitar, Laith Abualigah
Summary: This research paper introduces an effective and improved color correction model that combines ALS and RP to reduce color errors and enhance image quality. The proposed model is evaluated using various color models and it is found that it yields the least errors for the RGB color model.
Article
Computer Science, Artificial Intelligence
Nima Khodadadi, Laith Abualigah, Qasem Al-Tashi, Seyedali Mirjalili
Summary: The Chaos Game Optimization (CGO) is effective for single-objective optimization, but cannot handle multiple objectives. This study proposes a multi-objective CGO (MOCGO) algorithm that stores Pareto-optimal solutions and utilizes multi-objective optimization. MOCGO is evaluated using seventeen case studies and outperforms existing methods.
NEURAL COMPUTING & APPLICATIONS
(2023)
Article
Computer Science, Information Systems
Mohammad Otair, Laith Abualigah, Saif Tawfiq, Mohammad Alshinwan, Absalom E. Ezugwu, Raed Abu Zitar, Putra Sumari
Summary: In recent years, the determination of the ideal thresholding for picture segmentation has attracted increasing interest. However, existing techniques face issues such as long calculation times, high computational costs, and the need for accuracy improvements when finding appropriate thresholds for multilevel thresholding. This study investigates the capability of the Arithmetic Optimization Algorithm (AOA) to discover the best multilayer thresholding for picture segmentation. The AOA method utilizes the distributional nature of mathematical arithmetic operators and constructs candidate solutions based on the picture histogram, which are then updated according to the algorithm's features. The proposed approach is tested and evaluated against other well-known optimization methods using various assessment metrics.
MULTIMEDIA TOOLS AND APPLICATIONS
(2023)
Article
Computer Science, Information Systems
Ibrahim Obeidat, Ala Mughaid, Shadi AlZu'bi, Ahmed AL-Arjan, Rula AL-Amrat, Rathaa AL-Ajmi, Razan AL-Hayajneh, Belal Abuhaija, Laith Abualigah
Summary: The paper discusses the importance of protecting user information and the vulnerabilities that exist in security. It proposes an improved ancient rotor machine based on base64 encoding and a key exchange method based on OTP to encrypt data, and experiments show promising results.
MULTIMEDIA TOOLS AND APPLICATIONS
(2023)
Article
Engineering, Multidisciplinary
Maha Gharaibeh, Wlla Abedalaziz, Noor Aldeen Alawad, Hasan Gharaibeh, Ahmad Nasayreh, Mwaffaq El-Heis, Maryam Altalhi, Agostino Forestiero, Laith Abualigah
Summary: In this study, an innovative diagnostic approach for accurately locating multiple sclerosis (MS) and neuromyelitis optica (NMO) using machine learning algorithms applied to MRI scans was developed. The results demonstrated that KNN and SVM algorithms performed superiorly in differentiating between MS and NMO, and classifying active versus inactive states of MS, respectively. This advanced methodology provides clinicians with a highly accurate, efficient tool for diagnosing these diseases and has the potential to streamline treatment processes.
Article
Computer Science, Artificial Intelligence
Celal Cakiroglu, Sercan Demir, Mehmet Hakan Ozdemir, Batin Latif Aylak, Gencay Sariisik, Laith Abualigah
Summary: This study estimates the power produced in a wind turbine using six different regression algorithms based on machine learning. The XGBoost algorithm performs the best according to the R2 performance metric, while the LightGBM model is the most efficient in terms of computational speed. Wind speed is shown to have the most significant impact on the model predictions according to the SHAP algorithm.
EXPERT SYSTEMS WITH APPLICATIONS
(2024)
