Article
Computer Science, Artificial Intelligence
Xiaobing Yu, WangYing Xu, ChenLiang Li
Summary: Grey wolf optimizer is a novel swarm intelligent algorithm with superior optimization capacity. However, it is easy to trap into local optimum when solving complex and multimodal functions. The proposed opposition-based learning grey wolf optimizer incorporates a jumping rate to help the algorithm jump out of local optimum, and dynamically adjusts the coefficient to balance exploration and exploitation.
KNOWLEDGE-BASED SYSTEMS
(2021)
Article
Computer Science, Artificial Intelligence
Qingsong Fan, Haisong Huang, Kai Yang, Songsong Zhang, Liguo Yao, Qiaoqiao Xiong
Summary: The Equilibrium Optimizer (EO) is a physics-based metaheuristic algorithm that has competitive performance but with certain drawbacks. To address these issues, a modified version (m-EO) utilizing opposition-based learning and novel update rules is proposed, which significantly improves optimization precision and convergence speed. Experimental results demonstrate that the m-EO outperforms not only the original EO but also other state-of-the-art algorithms.
EXPERT SYSTEMS WITH APPLICATIONS
(2021)
Article
Engineering, Multidisciplinary
Vanisree Chandran, Prabhujit Mohapatra
Summary: The Grey Wolf Optimization algorithm (GWO) is a new swarm-based meta-heuristic algorithm that outperforms existing approaches. However, it faces limitations in exploitation ability and local optima when solving challenging optimization problems. To overcome these limitations, Enhanced Opposition-Based Learning (EOBL) is proposed to balance exploration and exploitation. The Enhanced Opposition-Based Grey Wolf Optimizer (EOBGWO) is developed to increase the effectiveness of the GWO algorithm.
ALEXANDRIA ENGINEERING JOURNAL
(2023)
Article
Computer Science, Artificial Intelligence
Changting Zhong, Gang Li, Zeng Meng, Wanxin He
Summary: In this work, the EOOBLE algorithm is proposed to solve high-dimensional global optimization problems. It combines the opposition-based learning strategy, the Levy flight strategy, and the evolutionary population dynamics strategy to improve the convergence capacity and performance. Experimental results show that the EOOBLE algorithm outperforms other state-of-the-art metaheuristic algorithms and variants of EO.
EXPERT SYSTEMS WITH APPLICATIONS
(2023)
Article
Computer Science, Hardware & Architecture
Angel Casas-Ordaz, Diego Oliva, Mario A. Navarro, Alfonso Ramos-Michel, Marco Perez-Cisneros
Summary: This article introduces a method for determining the threshold values for image segmentation using the Runge Kutta (RUN) optimization algorithm. By combining it with opposition-based learning (OBL), a hybrid algorithm called RUN-OBL is created, which can effectively solve high-dimensional problems. Experimental results demonstrate that the proposed approach performs better in terms of image segmentation and optimization of complex problems.
JOURNAL OF SUPERCOMPUTING
(2023)
Article
Mathematical & Computational Biology
Shikai Wang, Kangjian Sun, Wanying Zhang, Heming Jia
Summary: The paper proposes a modified ant lion optimizer algorithm based on opposition-based learning for optimizing multilevel thresholding in image segmentation, and experimental results show that the method outperforms others in terms of segmentation performance.
MATHEMATICAL BIOSCIENCES AND ENGINEERING
(2021)
Article
Computer Science, Artificial Intelligence
Essam H. Houssein, Kashif Hussain, Laith Abualigah, Mohamed Abd Elaziz, Waleed Alomoush, Gaurav Dhiman, Youcef Djenouri, Erik Cuevas
Summary: The paper introduces an enhanced version of the Marine Predators Algorithm (MPA) called MPA-OBL, which incorporates Opposition-Based Learning (OBL) to improve search efficiency and convergence. Through comprehensive experiments, MPA-OBL is shown to outperform other algorithms in solving complex optimization problems, demonstrating superior quality of solutions and faster convergence speed.
KNOWLEDGE-BASED SYSTEMS
(2021)
Article
Computer Science, Artificial Intelligence
Chi Ma, Haisong Huang, Qingsong Fan, Jianan Wei, Yiming Du, Weisen Gao
Summary: This paper proposes an improved grey wolf optimizer algorithm based on the Aquila Optimizer, which can enhance the global search ability and balance the exploration and exploitation stages.
EXPERT SYSTEMS WITH APPLICATIONS
(2022)
Article
Computer Science, Artificial Intelligence
Peng Gui, Fazhi He, Bingo Wing-Kuen Ling, Dengyi Zhang
Summary: This study introduces an optimization algorithm, the united equilibrium optimizer (UEO), which improves both exploration and exploitation capabilities by modifying the search structure of the equilibrium optimizer (EO) and adjusting it with dynamic parameters. The UEO outperforms other algorithms in most cases, as demonstrated through benchmark tests and practical problems.
KNOWLEDGE-BASED SYSTEMS
(2021)
Article
Computer Science, Information Systems
Priteesha Sarangi, Prabhujit Mohapatra
Summary: This paper introduces an Evolved Opposition-based Learning mechanism for the Mountain Gazelle Optimizer (EOBMGO) to overcome its limitations in dealing with higher dimensions and local optima. Experimental results and statistical tests demonstrate that EOBMGO outperforms existing algorithms, making it an efficient approach for complex optimization challenges.
JOURNAL OF KING SAUD UNIVERSITY-COMPUTER AND INFORMATION SCIENCES
(2023)
Article
Computer Science, Interdisciplinary Applications
Laith Abualigah, Dalia Yousri, Mohamed Abd Elaziz, Ahmed A. Ewees, Mohammed A. A. Al-qaness, Amir H. Gandomi
Summary: This paper introduces a novel population-based optimization method, AO, inspired by the behaviors of eagles during hunting. Through a series of experiments, the superior performance of AO in finding optimal solutions for various problems is demonstrated and compared with other meta-heuristic methods.
COMPUTERS & INDUSTRIAL ENGINEERING
(2021)
Article
Computer Science, Artificial Intelligence
Essam H. Houssein, Mohamed A. Mahdy, Ahmed Fathy, Hegazy Rezk
Summary: The study introduces a new method for maximum power point tracking in photovoltaic systems, the MPAOBL-GWO algorithm, which combines Opposition Based Learning strategy and Grey Wolf Optimizer to enhance global search efficiency and prevent local optima.
EXPERT SYSTEMS WITH APPLICATIONS
(2021)
Article
Computer Science, Artificial Intelligence
Dong Pang, Xinyi Le, Xinping Guan
Summary: This study introduces a novel differentiable search method based on reinforcement learning to optimize the architecture-parameter optimization problem in neural architecture search, aiming to improve computation efficiency, network precision, and robustness. By utilizing a double-loop algorithm to address the optimization problem in the searched super-network, the method alternates between optimizing the super-network and the meta-optimizer, leading to faster and more robust convergence.
KNOWLEDGE-BASED SYSTEMS
(2021)
Article
Computer Science, Artificial Intelligence
Susheel Kumar Joshi
Summary: This paper proposes an improved gravitational search algorithm that incorporates chaos-embedded opposition-based learning and a sine-cosine based chaotic gravitational constant for stagnation-free search of global optima. Experimental results demonstrate the superiority of the proposed algorithm over conventional meta-heuristics and recent GSA variants in various benchmark and test problems.
APPLIED INTELLIGENCE
(2023)
Article
Computer Science, Artificial Intelligence
Min Zhang, Jie-Sheng Wang, Jia-Ning Hou, Hao-Ming Song, Xu-Dong Li, Fu-Jun Guo
Summary: In this paper, a ReliefF-guided novel binary equilibrium optimizer (RG-NBEO) is proposed for feature selection. The proposed method can effectively improve the classification accuracy while reducing the dimensionality of the dataset.
ARTIFICIAL INTELLIGENCE REVIEW
(2023)
Article
Operations Research & Management Science
Alireza Goli, Ali Ala, Seyedali Mirjalili
Summary: This paper proposes a mathematical formulation and solution method to optimize organ transplant supply chain under shipment time uncertainty. The proposed model considers the fuzzy uncertainty of organ demands and transportation time, and the simulation-based optimization using credibility theory. The numerical results show that the optimal credibility level is between 0.2 and 0.6 in all tested cases.
ANNALS OF OPERATIONS RESEARCH
(2023)
Review
Computer Science, Interdisciplinary Applications
Ali Mohammadi, Farid Sheikholeslam, Seyedali Mirjalili
Summary: This work provides an overview of the state-of-the-art inclined planes system optimization (IPO) algorithms, evaluating their variants, applications, statistical evaluation, and analysis. The study finds that the use of bio-operators to improve the standard version of IPO yields better performance, while versions without control parameters show intelligent exploration and exploitation. SIPO + M achieves the optimal performance.
ARCHIVES OF COMPUTATIONAL METHODS IN ENGINEERING
(2023)
Article
Computer Science, Artificial Intelligence
Payel Pramanik, Souradeep Mukhopadhyay, Seyedali Mirjalili, Ram Sarkar
Summary: Breast cancer is a common malignancy in women, and early detection is crucial. In this research, a method for classifying breast masses using mammograms is proposed. Deep features are extracted using the VGG16 model with an attention mechanism, and an optimal features subset is obtained using a meta-heuristic algorithm. The proposed model shows successful identification and differentiation of malignant and healthy breasts.
NEURAL COMPUTING & APPLICATIONS
(2023)
Article
Computer Science, Artificial Intelligence
Halil Yigit, Satilmis Urgun, Seyedali Mirjalili
Summary: This study employs various metaheuristic algorithms to find the best optimization framework for identifying switching moments in an 11-level multilevel inverter. Simulation results show that the Moth Flame Optimizer (MFO) outperforms other algorithms in terms of Total Harmonic Distortion (THD) minimization, convergence rate, single iteration time, and robustness.
NEURAL COMPUTING & APPLICATIONS
(2023)
Article
Computer Science, Interdisciplinary Applications
Dler O. Hasan, Aso M. Aladdin, Hardi Sabah Talabani, Tarik Ahmed Rashid, Seyedali Mirjalili
Summary: This paper discusses the use of the bidirectional A* search algorithm with three heuristics to solve the Fifteen Puzzle problem, effectively managing the large state space and reducing the number of generated states.
Review
Computer Science, Interdisciplinary Applications
Mohammad H. Nadimi-Shahraki, Hoda Zamani, Zahra Asghari Varzaneh, Seyedali Mirjalili
Summary: Despite the success of the whale optimization algorithm (WOA) in solving optimization problems, there are still many issues that need to be addressed. This paper critically analyzes WOA and reviews its developments in the past 5 years, aiming to find effective techniques and algorithms for improvement and hybridization.
ARCHIVES OF COMPUTATIONAL METHODS IN ENGINEERING
(2023)
Review
Computer Science, Interdisciplinary Applications
Mohammed Azmi Al-Betar, Mohammed A. Awadallah, Sharif Naser Makhadmeh, Zaid Abdi Alkareem Alyasseri, Ghazi Al-Naymat, Seyedali Mirjalili
Summary: The Marine Predators Algorithm (MPA) is a nature-inspired optimizer based on the foraging mechanisms of ocean predators. It has become popular for its derivative-free, parameterless, and easy-to-use features, leading to its wide application in various optimization problems. This review paper analyzes the growth and performance of MPA based on 102 research papers. It discusses the inspirations and theoretical concepts of MPA, focusing on its convergence behavior. The review also examines the versions of MPA proposed to improve its performance on real-world optimization problems and explores the diverse optimization applications using MPA as the main solver.
ARCHIVES OF COMPUTATIONAL METHODS IN ENGINEERING
(2023)
Article
Computer Science, Information Systems
Satilmis Urgun, Halil Yigit, Seyedali Mirjalili
Summary: Multilevel inverters (MLI) are widely used in high-power applications. The selective harmonic elimination (SHE) method is employed to reduce switching and eliminate desired harmonics. Classical methods have limitations, so metaheuristic algorithms are used to find better solutions. Extensive analysis of 22 metaheuristic algorithms was performed, and the methods SPBO, BMO, GA, GWO, MFO, and SPSA were found to offer the best performance.
Article
Mathematics
Ali El Romeh, Seyedali Mirjalili, Faiza Gul
Summary: This study proposes a novel hybrid optimization method called Hybrid Vulture-Coordinated Multi-Robot Exploration (HVCME), which combines Coordinated Multi-Robot Exploration (CME) and African Vultures Optimization Algorithm (AVOA) to optimize the construction of a finite map in multi-robot exploration. Experimental results show that HVCME outperforms four other similar methods, demonstrating its effectiveness in optimizing the construction of a finite map in an unknown indoor environment.
Article
Mathematics
Mohammad H. H. Nadimi-Shahraki, Hoda Zamani, Ali Fatahi, Seyedali Mirjalili
Summary: Moth-flame optimization (MFO) is a simple yet widely used problem solver for different optimization problems. However, MFO and its variants suffer from poor population diversity, resulting in premature convergence and lower solution quality. To address this issue, an enhanced algorithm called MFO-SFR was developed, which utilizes an effective stagnation finding and replacing (SFR) strategy to maintain population diversity during the optimization process. Extensive evaluations on benchmark functions and comparison with competitors demonstrated that the proposed MFO-SFR algorithm outperformed MFO variants and state-of-the-art metaheuristic algorithms in solving complex global optimization problems, with an effectiveness of 91.38%.
Article
Computer Science, Interdisciplinary Applications
Nazar K. Hussein, Mohammed Qaraad, Souad Amjad, M. A. Farag, Saima Hassan, Seyedali Mirjalili, Mostafa A. Elhosseini
Summary: This research paper addresses the limitations of the Moth-Flame Optimization (MFO) algorithm and proposes an improved version called GMSMFO. The performance of GMSMFO is evaluated using benchmark tests and compared to other metaheuristic algorithms, showing its competitive advantage. The main contribution of this study lies in the enhanced diversity and exploration/exploitation balance of GMSMFO.
JOURNAL OF COMPUTATIONAL DESIGN AND ENGINEERING
(2023)
Article
Computer Science, Artificial Intelligence
Yichao He, Hailu Sun, Yuan Wang, Xinlu Zhang, Seyedali Mirjalili
Summary: This paper defines the operations of intersection, union, complement, difference, and symmetric difference for 0-1 vectors on {0,1}n based on set operations, and proves the isomorphism between the algebraic system on {0,1}n and set algebra on the power set P(S2) of set S2. A simple and fast implementation method of set algebra is proposed. Then, symmetric difference and asymmetric mutation operators are introduced based on set algebra, offering global exploration and local exploitation capabilities respectively. A novel algebraic evolutionary algorithm called SAHA is proposed for solving binary optimization problems. Experimental results show that SAHA achieves excellent calculation results and outperforms state-of-the-art algorithms in terms of speed.
APPLIED SOFT COMPUTING
(2023)
Article
Computer Science, Artificial Intelligence
Anurup Naskar, Rishav Pramanik, S. K. Sabbir Hossain, Seyedali Mirjalili, Ram Sarkar
Summary: In the era of data-driven digital society, there is a need for optimized solutions that can reduce operation costs and increase productivity. Machine learning and data mining algorithms have limitations when processing large amounts of data, especially when dealing with redundant and non-important information. Researchers have developed feature selection algorithms to address this issue, and metaheuristic based optimization algorithms have proven to be effective in solving feature selection problems.
EXPERT SYSTEMS WITH APPLICATIONS
(2023)
Article
Environmental Sciences
Payam Gouran, Mohammad H. Nadimi-Shahraki, Amir Masoud Rahmani, Seyedali Mirjalili
Summary: In intelligent traffic control systems, the use of loop detectors to extract features for imputation of missing data is insufficient for accurate results. To address this issue, a data enrichment imputation method called EIM-LD is proposed, which incorporates statistical multi-class labeling to increase imputation accuracy for different missing patterns and ratios. The proposed method enriches clean data by adding statistical multi-class labels, and then uses a data model constructed from labeled clean data to label the missed-volume data. The experimental and statistical results demonstrate the effectiveness of the proposed method in enriching real data and improving imputation accuracy.
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)
Review
Computer Science, Artificial Intelligence
Wei Gao, Shuangshuang Ge
Summary: This study provides a comprehensive review of slope stability research based on artificial intelligence methods, focusing on slope stability computation and evaluation. The review covers studies using quasi-physical intelligence methods, simulated evolutionary methods, swarm intelligence methods, hybrid intelligence methods, artificial neural network methods, vector machine methods, and other intelligence methods. The merits, demerits, and state-of-the-art research advancement of these studies are analyzed, and possible research directions for slope stability investigation based on artificial intelligence methods are suggested.
EXPERT SYSTEMS WITH APPLICATIONS
(2024)
Article
Computer Science, Artificial Intelligence
Khuong Le Nguyen, Hoa Thi Trinh, Saeed Banihashemi, Thong M. Pham
Summary: This study investigated the influence of input parameters on the shear strength of RC squat walls and found that ensemble learning models, particularly XGBoost, can effectively predict the shear strength. The axial load had a greater influence than reinforcement ratio, and longitudinal reinforcement had a more significant impact compared to horizontal and vertical reinforcement. The performance of XGBoost model outperforms traditional design models and reducing input features still yields reliable predictions.
EXPERT SYSTEMS WITH APPLICATIONS
(2024)
Article
Computer Science, Artificial Intelligence
Bo Hu, Huiyan Zhang, Xiaoyi Wang, Li Wang, Jiping Xu, Qian Sun, Zhiyao Zhao, Lei Zhang
Summary: A deep hierarchical echo state network (DHESN) is proposed to address the limitations of shallow coupled structures. By using transfer entropy, candidate variables with strong causal relationships are selected and a hierarchical reservoir structure is established to improve prediction accuracy. Simulation results demonstrate that DHESN performs well in predicting algal bloom.
EXPERT SYSTEMS WITH APPLICATIONS
(2024)
Article
Computer Science, Artificial Intelligence
Limin Wang, Lingling Li, Qilong Li, Kuo Li
Summary: This paper discusses the urgency of learning complex multivariate probability distributions due to the increase in data variability and quantity. It introduces a highly scalable classifier called TAN, which utilizes maximum weighted spanning tree (MWST) for graphical modeling. The paper theoretically proves the feasibility of extending one-dependence MWST to model high-dependence relationships and proposes a heuristic search strategy to improve the fitness of the extended topology to data. Experimental results demonstrate that this algorithm achieves a good bias-variance tradeoff and competitive classification performance compared to other high-dependence or ensemble learning algorithms.
EXPERT SYSTEMS WITH APPLICATIONS
(2024)
Article
Computer Science, Artificial Intelligence
Zhejing Hu, Gong Chen, Yan Liu, Xiao Ma, Nianhong Guan, Xiaoying Wang
Summary: Anxiety is a prevalent issue and music therapy has been found effective in reducing anxiety. To meet the diverse needs of individuals, a novel model called the spatio-temporal therapeutic music transfer model (StTMTM) is proposed.
EXPERT SYSTEMS WITH APPLICATIONS
(2024)
Article
Computer Science, Artificial Intelligence
Nur Ezlin Zamri, Mohd. Asyraf Mansor, Mohd Shareduwan Mohd Kasihmuddin, Siti Syatirah Sidik, Alyaa Alway, Nurul Atiqah Romli, Yueling Guo, Siti Zulaikha Mohd Jamaludin
Summary: In this study, a hybrid logic mining model was proposed by combining the logic mining approach with the Modified Niche Genetic Algorithm. This model improves the generalizability and storage capacity of the retrieved induced logic. Various modifications were made to address other issues. Experimental results demonstrate that the proposed model outperforms baseline methods in terms of accuracy, precision, specificity, and correlation coefficient.
EXPERT SYSTEMS WITH APPLICATIONS
(2024)
Article
Computer Science, Artificial Intelligence
David Jacob Kedziora, Tien-Dung Nguyen, Katarzyna Musial, Bogdan Gabrys
Summary: The paper addresses the problem of efficiently optimizing machine learning solutions by reducing the configuration space of ML pipelines and leveraging historical performance. The experiments conducted show that opportunistic/systematic meta-knowledge can improve ML outcomes, and configuration-space culling is optimal when balanced. The utility and impact of meta-knowledge depend on various factors and are crucial for generating informative meta-knowledge bases.
EXPERT SYSTEMS WITH APPLICATIONS
(2024)
Article
Computer Science, Artificial Intelligence
G. Sophia Jasmine, Rajasekaran Stanislaus, N. Manoj Kumar, Thangamuthu Logeswaran
Summary: In the context of a rapidly expanding electric vehicle market, this research investigates the ideal locations for EV charging stations and capacitors in power grids to enhance voltage stability and reduce power losses. A hybrid approach combining the Fire Hawk Optimizer and Spiking Neural Network is proposed, which shows promising results in improving system performance. The optimization approach has the potential to enhance the stability and efficiency of electric grids.
EXPERT SYSTEMS WITH APPLICATIONS
(2024)
Article
Computer Science, Artificial Intelligence
Zhijiang Wu, Guofeng Ma
Summary: This study proposes a natural language processing-based framework for requirement retrieval and document association, which can help to mine and retrieve documents related to project managers' requirements. The framework analyzes the ontology relevance and emotional preference of requirements. The results show that the framework performs well in terms of iterations and threshold, and there is a significant matching between the retrieved documents and the requirements, which has significant managerial implications for construction safety management.
EXPERT SYSTEMS WITH APPLICATIONS
(2024)
Article
Computer Science, Artificial Intelligence
Yung-Kuan Chan, Chuen-Horng Lin, Yuan-Rong Ben, Ching-Lin Wang, Shu-Chun Yang, Meng-Hsiun Tsai, Shyr-Shen Yu
Summary: This study proposes a novel method for dog identification using nose-print recognition, which can be applied to controlling stray dogs, locating lost pets, and pet insurance verification. The method achieves high recognition accuracy through two-stage segmentation and feature extraction using a genetic algorithm.
EXPERT SYSTEMS WITH APPLICATIONS
(2024)
Article
Computer Science, Artificial Intelligence
Shaohua Song, Elena Tappia, Guang Song, Xianliang Shi, T. C. E. Cheng
Summary: This study aims to optimize supplier selection and demand allocation decisions for omni-channel retailers in order to achieve supply chain resilience. It proposes a two-phase approach that takes into account various factors such as supplier evaluation and demand allocation.
EXPERT SYSTEMS WITH APPLICATIONS
(2024)
Article
Computer Science, Artificial Intelligence
Jinyan Hu, Yanping Jiang
Summary: This paper examines the allocation problem of shared parking spaces considering parking unpunctuality and no-shows. It proposes an effective approach using sample average approximation (SAA) combined with an accelerating Benders decomposition (ABD) algorithm to solve the problem. The numerical experiments demonstrate the significance of supply-demand balance for the operation and user satisfaction of the shared parking system.
EXPERT SYSTEMS WITH APPLICATIONS
(2024)
Review
Computer Science, Artificial Intelligence
Soroor Motie, Bijan Raahemi
Summary: Financial fraud is a persistent problem in the finance industry, but Graph Neural Networks (GNNs) have emerged as a powerful tool for detecting fraudulent activities. This systematic review provides a comprehensive overview of the current state-of-the-art technologies in using GNNs for financial fraud detection, identifies gaps and limitations in existing research, and suggests potential directions for future research.
EXPERT SYSTEMS WITH APPLICATIONS
(2024)
Review
Computer Science, Artificial Intelligence
Enhao Ning, Changshuo Wang, Huang Zhang, Xin Ning, Prayag Tiwari
Summary: This review provides a detailed overview of occluded person re-identification methods and conducts a systematic analysis and comparison of existing deep learning-based approaches. It offers important theoretical and practical references for future research in the field.
EXPERT SYSTEMS WITH APPLICATIONS
(2024)
Article
Computer Science, Artificial Intelligence
Jiajun Ma, Songyu Hu, Jianzhong Fu, Gui Chen
Summary: The article presents a novel visual hierarchical attention detector for multi-scale defect location and classification, utilizing texture, semantic, and instance features of defects through a hierarchical attention mechanism, achieving multi-scale defect detection in bearing images with complex backgrounds.
EXPERT SYSTEMS WITH APPLICATIONS
(2024)