Article
Computer Science, Artificial Intelligence
Babak Nouri-Moghaddam, Mehdi Ghazanfari, Mohammad Fathian
Summary: The study introduces a multi-objective feature selection algorithm based on the forest optimization algorithm, showing that it can reduce classification errors and feature numbers in most cases.
EXPERT SYSTEMS WITH APPLICATIONS
(2021)
Article
Computer Science, Artificial Intelligence
Adel Got, Abdelouahab Moussaoui, Djaafar Zouache
Summary: This paper introduces a novel hybrid filter-wrapper feature selection approach using whale optimization algorithm, which optimizes multiple objective functions simultaneously. Experimental results demonstrate the efficiency of the proposed algorithm on twelve benchmark datasets, showing its ability to obtain subsets with smaller number of features and excellent classification accuracy.
EXPERT SYSTEMS WITH APPLICATIONS
(2021)
Article
Computer Science, Information Systems
An-Da Li, Bing Xue, Mengjie Zhang
Summary: This paper proposes a feature selection method to identify key quality features in complex manufacturing processes. A multi-objective binary particle swarm optimization algorithm is proposed, which includes three new components to optimize a bi-objective feature selection model. Experimental results show that this method can identify a small number of key quality features with good predictive ability.
INFORMATION SCIENCES
(2023)
Article
Computer Science, Artificial Intelligence
Pradip Dhal, Chandrashekhar Azad
Summary: In this study, a binary version of the hybrid two-phase multi-objective FS approach based on PSO and GWO is proposed. The approach aims to minimize classification error rate and reduce the number of selected features. By utilizing global and local search strategies, the method shows efficient and effective performance in selecting prominent features in high-dimensional data.
APPLIED SOFT COMPUTING
(2021)
Article
Computer Science, Artificial Intelligence
Dipanjyoti Paul, Anushree Jain, Sriparna Saha, Jimson Mathew
Summary: This paper presents an adaptive feature selection algorithm for multi-label classification scenarios, real-time selecting the optimal feature subset online. Through a three-phase filtering process, the algorithm improves the accuracy and efficiency of feature selection.
KNOWLEDGE-BASED SYSTEMS
(2021)
Article
Computer Science, Information Systems
Himansu Das, Bighnaraj Naik, H. S. Behera
Summary: This paper proposes a feature selection approach based on Jaya optimization algorithm, which improves the performance of supervised machine learning techniques by reducing the dimensions of the feature space. Experimental results show that this approach achieves higher classification accuracy compared to other feature selection methods.
JOURNAL OF KING SAUD UNIVERSITY-COMPUTER AND INFORMATION SCIENCES
(2022)
Article
Computer Science, Interdisciplinary Applications
Yanan Zhang, Renjing Liu, Xin Wang, Huiling Chen, Chengye Li
Summary: This paper introduces an improved Harris hawks optimization (HHO) method for global optimization and feature selection tasks. By embedding the salp swarm algorithm (SSA) into the original HHO, the proposed IHHO enhances the search ability of the optimizer and broadens its application scope.
ENGINEERING WITH COMPUTERS
(2021)
Article
Computer Science, Artificial Intelligence
Divya Bairathi, Dinesh Gopalani
Summary: The proposed improved salp swarm algorithm, by integrating multiple elements, enhances exploration and exploitation capabilities, making it more effective in solving complex multimodal problems.
Article
Computer Science, Information Systems
Djaafar Zouache, Adel Got, Deemah Alarabiat, Laith Abualigah, El-Ghazali Talbi
Summary: Feature selection is important in machine learning for improving classification capabilities and reducing dataset dimensionality. However, there are limited studies on multi-objective feature selection. In this paper, we propose a novel algorithm that combines quantum computing, Firefly Algorithm (FA), and Particle Swarm Optimizer (PSO) to address this problem. Our algorithm outperforms other algorithms in terms of feature subset size and classification accuracy, as demonstrated by our COVID-19 detection system.
MULTIMEDIA TOOLS AND APPLICATIONS
(2023)
Article
Computer Science, Information Systems
Ibrahim Aljarah, Hossam Faris, Ali Asghar Heidari, Majdi M. Mafarja, Ala' M. Al-Zoubi, Pedro A. Castillo, Juan J. Merelo
Summary: This paper introduces a binary multi-objective variant of MVO (MOMVO) for dealing with feature selection tasks, which addresses the local optima stagnation issue of standard MOMVO by using the memory concept and personal best of universes. The experimental results demonstrate that the proposed binary MOMVO approach effectively eliminates irrelevant and/or redundant features and maintains a minimum classification error rate across different datasets compared to popular feature selection techniques. Additionally, the approach outperforms state-of-the-art multi-objective optimization algorithms for feature selection on 14 benchmark datasets.
Article
Computer Science, Artificial Intelligence
Warda M. Shaban, Asmaa H. Rabie, Ahmed Saleh, M. A. Abo-Elsoud
Summary: COVID-19, a global infectious disease, requires early detection of infected patients for effective treatment and disease control. This paper introduces a new strategy called DBNB, which uses APSO to select informative features for accurate diagnosis of COVID-19 patients. Experimental results show that DBNB outperforms recent COVID-19 diagnose strategies in accuracy and time efficiency.
PATTERN RECOGNITION
(2021)
Article
Computer Science, Interdisciplinary Applications
Songwei Zhao, Pengjun Wang, Xuehua Zhao, Hamza Turabieh, Majdi Mafarja, Huiling Chen
Summary: This paper focuses on the performance of an improved algorithm AGSSA based on the SSA algorithm, introducing adaptive control parameters and elite gray wolf domination strategy to enhance local search and global optimization capabilities, showing significantly better performance than competitive metaheuristic algorithms.
ENGINEERING WITH COMPUTERS
(2022)
Article
Biology
Dongwan Lu, Yinggao Yue, Zhongyi Hu, Minghai Xu, Yinsheng Tong, Hanjie Ma
Summary: In this paper, a fuzzy k-nearest neighbor method based on the improved binary salp swarm algorithm (IBSSA-FKNN) is proposed for the early diagnosis of Alzheimer's disease (AD). The method is validated on multiple benchmark datasets and effectively distinguishes between patients with mild cognitive impairment (MCI), AD, and normal controls (NC).
COMPUTERS IN BIOLOGY AND MEDICINE
(2023)
Article
Computer Science, Artificial Intelligence
Yu Xue, Haokai Zhu, Jiayu Liang, Adam Slowik
Summary: Feature selection is a crucial pre-processing technique for classification, aiming to enhance classification accuracy by removing irrelevant or redundant features. This study introduces a multi-objective genetic algorithm with an adaptive operator selection mechanism, which effectively addresses high-dimensional feature selection problems.
KNOWLEDGE-BASED SYSTEMS
(2021)
Article
Computer Science, Artificial Intelligence
Abdolreza Rashno, Milad Shafipour, Sadegh Fadaei
Summary: This paper introduces a novel multi-objective particle swarm optimization feature selection method. It decodes feature vectors as particles and ranks them in a two-dimensional optimization space. The proposed method incorporates feature ranks to update particle velocity and position during the optimization process. Experimental results demonstrate the effectiveness of the method in finding Pareto Fronts of the best particles in multi-objective optimization space.
KNOWLEDGE-BASED SYSTEMS
(2022)
Article
Biochemical Research Methods
Jianfu Xia, Zhennao Cai, Ali Asghar Heidari, Yinghai Ye, Huiling Chen, Zhifang Pan
Summary: This paper introduces a quasi-reflection moth-flame optimization algorithm called QRMFO to strengthen the property of ordinary MFO and apply it in various application fields to overcome shortcomings.
CURRENT BIOINFORMATICS
(2023)
Correction
Computer Science, Interdisciplinary Applications
Ali Mohammadi, Farid Sheikholeslam, Seyedali Mirjalili
ARCHIVES OF COMPUTATIONAL METHODS IN ENGINEERING
(2023)
Article
Computer Science, Artificial Intelligence
Mohammed Qaraad, Souad Amjad, Nazar K. Hussein, Seyedali Mirjalili, Mostafa A. Elhosseini
Summary: This study proposes a time-based leadership particle swarm-based Salp (TPSOSA) algorithm to address the limitations of Particle swarm optimization (PSO). TPSOSA is a novel search technique that solves the issues of population diversity, exploitation and exploration imbalance, and premature convergence in the PSO algorithm. The experimental data and statistical tests show that TPSOSA is competitive and often superior to other algorithms.
ARTIFICIAL INTELLIGENCE REVIEW
(2023)
Article
Computer Science, Artificial Intelligence
Zhangze Xu, Ali Asghar Heidari, Fangjun Kuang, Ashraf Khalil, Majdi Mafarja, Siyang Zhang, Huiling Chen, Zhifang Pan
Summary: Grasshopper Optimization Algorithm (GOA) is a recent meta-heuristic algorithm that imitates the biological features of grasshoppers for single-objective optimization cases. However, the basic GOA has issues with early convergence and slow convergence. To address these problems, this study proposes an Enhanced GOA (EGOA) that incorporates elite opposition-based learning and bare-bones Gaussian strategy to improve global and local search capabilities. Experimental results on benchmark tasks and practical applications demonstrate the effectiveness of EGOA in optimization and feature selection.
EXPERT SYSTEMS WITH APPLICATIONS
(2023)
Review
Automation & Control Systems
Huiling Chen, Chenyang Li, Majdi Mafarja, Ali Asghar Heidari, Yi Chen, Zhennao Cai
Summary: This paper provides a comprehensive review of critical studies related to the development of Slime Mould Algorithm (SMA), including an analysis of advanced versions of SMA and its application domains. The survey shows that SMA outperforms established metaheuristics in terms of speed and accuracy, and suggests possible future research directions.
INTERNATIONAL JOURNAL OF SYSTEMS SCIENCE
(2023)
Article
Computer Science, Information Systems
Ali Safaa Sadiq, Amin Abdollahi Dehkordi, Seyedali Mirjalili, Jingwei Too, Prashant Pillai
Summary: This article focuses on developing a new trustworthy and efficient routing mechanism for routing data traffic over IoT-FinTech mobile networks. A new nonlinear Levy Brownian generalized normal distribution optimization (NLBGNDO) algorithm is proposed to solve the problem of finding an optimal path from source to destination sensor nodes. The proposed mechanism maintains wise and efficient decisions over the selection period in comparison with other methods.
IEEE INTERNET OF THINGS JOURNAL
(2023)
Article
Computer Science, Artificial Intelligence
Xiao Yang, Rui Wang, Dong Zhao, Fanhua Yu, Chunyu Huang, Ali Asghar Heidari, Zhennao Cai, Sami Bourouis, Abeer D. Algarni, Huiling Chen
Summary: The sine cosine algorithm (SCA) is a well-known optimization algorithm that has gained attention for its simple structure and excellent optimization capabilities. To overcome the limitations of the original SCA, a modified variant called ARSCA is proposed, which incorporates adaptive quadratic interpolation mechanism and rounding mechanism. Experimental results demonstrate that ARSCA outperforms its competitors in terms of solution quality and ability to escape local optima.
EXPERT SYSTEMS WITH APPLICATIONS
(2023)
Article
Engineering, Multidisciplinary
Xinxin He, Weifeng Shan, Ruilei Zhang, Ali Asghar Heidari, Huiling Chen, Yudong Zhang
Summary: Swarm intelligence algorithms have gained attention for their flexible solutions to complex real-world problems. The colony predation algorithm (CPA), a new algorithm inspired by nature's predatory habits, has been proposed. However, CPA lacks exploratory ability and struggles with local optima solutions. To address this, an improved variant (OLCPA) with an orthogonal learning strategy is proposed. Furthermore, an OLCPA-CNN model, utilizing OLCPA to optimize the parameters of a convolutional neural network, is introduced. Experimental results demonstrate the superior performance of OLCPA compared to traditional metaheuristics and advanced algorithms, as well as the high accuracy of the OLCPA-CNN model in classifying datasets.
Article
Energy & Fuels
Xuemeng Weng, Ali Asghar Heidari, Huiling Chen
Summary: Accurate determination of photovoltaic (PV) parameters is crucial for the reliable operation of solar systems, uninterrupted power supply, and efficient energy management. This paper proposes a novel parameter extraction model using the Q-learning-based multistrategy improved shuffled frog leading algorithm (CRNSFLA). The comprehensive test results show that CRNSFLA outperforms existing algorithms in parameter extraction problems, making it an effective tool for solar cell parameter extractions.
INTERNATIONAL JOURNAL OF ENERGY RESEARCH
(2023)
Article
Education & Educational Research
Sourajit Ghosh, Md. Sarwar Kamal, Linkon Chowdhury, Biswarup Neogi, Nilanjan Dey, Robert Simon Sherratt
Summary: Students are crucial for a nation's future. Tailoring higher education courses to students' interests is a major challenge. AI and ML approaches have been employed to study student behavior, but concerns about interpretability and understandability remain due to the black-box nature of most algorithms.
EDUCATION AND INFORMATION TECHNOLOGIES
(2023)
Article
Engineering, Industrial
Reza Shahabi-Shahmiri, Thomas S. Kyriakidis, Mohammad Ghasemi, Seyed-Ali Mirnezami, Seyedali Mirjalili
Summary: This study proposes a bi-objective mixed integer linear programming framework for the multi-mode resource-constrained project scheduling problem under uncertain conditions. The framework considers minimizing project makespan and resource costs as objectives, and takes into account renewable and non-renewable resources and different modes for activities implementation. It efficiently addresses model uncertainty by using a fuzzy chance constrained programming method and extending two robust possibilistic programming models. The capability of the framework is validated using problem instances from PSPLIB and MMLIB, and a computational comparison is presented to assess the performance of the possibilistic programming models.
INTERNATIONAL JOURNAL OF SYSTEMS SCIENCE-OPERATIONS & LOGISTICS
(2023)
Article
Engineering, Multidisciplinary
Yaoyao Lin, Ali Asghar Heidari, Shuihua Wang, Huiling Chen, Yudong Zhang
Summary: The Hunger Games Search (HGS) is an innovative optimizer inspired by social animals' collaborative foraging activities. This study proposes two adjusted strategies, LS-OBL and RM, to enhance the original HGS algorithm. Experimental results demonstrate the effectiveness of these strategies and show that the improved algorithm, RLHGS, outperforms other state-of-the-art algorithms in various test suites. The application of RLHGS to real-world engineering optimization problems further supports its efficiency and value.
Article
Computer Science, Information Systems
Nima Khodadadi, Ehsan Khodadadi, Qasem Al-Tashi, El-Sayed M. El-Kenawy, Laith Abualigah, Said Jadid Abdulkadir, Alawi Alqushaibi, Seyedali Mirjalili
Summary: This paper proposes a binary version of the Arithmetic Optimization Algorithm (BAOA) to tackle the feature selection problem in classification. The BAOA uses the distribution behavior of main arithmetic operators and outperforms other binary algorithms in terms of classification accuracy, selected features, and optimum fitness values.
Article
Computer Science, Information Systems
Sunday O. Oladejo, Stephen O. Ekwe, Lateef A. Akinyemi, Seyedali A. Mirjalili
Summary: Due to the limitations of single optimisation algorithms, new optimisation techniques are required. This paper proposes a novel metaheuristic called the deep sleep optimiser (DSO), which mimics human sleeping patterns to solve optimisation problems. The DSO is modelled on the rise and fall of homeostatic pressure during the deep sleep stage of human sleep. Its performance is demonstrated and compared with other metaheuristics using various functions and problems, showing that the DSO performs well and often outperforms others.
Article
Computer Science, Artificial Intelligence
Ahmad Taheri, Keyvan RahimiZadeh, Amin Beheshti, Jan Baumbach, Ravipudi Venkata Rao, Seyedali Mirjalili, Amir H. Gandomi
Summary: In this paper, a novel evolutionary optimization algorithm called Partial Reinforcement Optimizer (PRO) is introduced. The PRO algorithm is based on the psychological theory of partial reinforcement effect (PRE) and is mathematically modeled to solve global optimization problems. Experimental results demonstrate that the PRO algorithm outperforms existing meta-heuristic algorithms in terms of accuracy and robustness.
EXPERT SYSTEMS WITH APPLICATIONS
(2024)
Article
Computer Science, Interdisciplinary Applications
Xiaolin Wang, Liyi Zhan, Yong Zhang, Teng Fei, Ming-Lang Tseng
Summary: This study proposes an environmental cold chain logistics distribution center location model to reduce transportation costs and carbon emissions. It also introduces a hybrid arithmetic whale optimization algorithm to overcome the limitations of the conventional algorithm.
COMPUTERS & INDUSTRIAL ENGINEERING
(2024)
Article
Computer Science, Interdisciplinary Applications
Hong-yu Liu, Shou-feng Ji, Yuan-yuan Ji
Summary: This study proposes an architecture that utilizes Ethereum to investigate the production-inventory-delivery problem in Physical Internet (PI), and develops an iterative heuristic algorithm that outperforms other algorithms. However, due to gas prices and consumption, blockchain technology may not always be the optimal solution.
COMPUTERS & INDUSTRIAL ENGINEERING
(2024)
Article
Computer Science, Interdisciplinary Applications
Paraskevi Th. Zacharia, Elias K. Xidias, Andreas C. Nearchou
Summary: This article discusses the assembly line balancing problem in production lines with collaborative robots. Collaborative robots have the potential to improve automation, productivity, accuracy, and flexibility in manufacturing. The article explores the use of a problem-specific metaheuristic to solve this complex problem under uncertainty.
COMPUTERS & INDUSTRIAL ENGINEERING
(2024)