Article
Computer Science, Artificial Intelligence
Yifeng Li, Ying Tan
Summary: In this paper, a theoretical model of fireworks algorithm based on search space partition is proposed, analyzed, and implemented. Experimental results show that the proposed algorithm outperforms previous variants of fireworks algorithm significantly, and achieves competitive results compared with state-of-the-art evolutionary algorithms.
INTERNATIONAL JOURNAL OF INTELLIGENT SYSTEMS
(2022)
Article
Engineering, Multidisciplinary
Yanqiang Tang, Chenghai Li, Song Li, Bo Cao, Chen Chen
Summary: This paper proposes an improved sparrow search algorithm (ISSA) to address the inherent problems of swarm intelligence algorithm. The ISSA introduces flight behavior from bird swarm algorithm and crossover/mutation from genetic algorithm to maintain population diversity and enhance optimization ability. These improvements effectively prevent falling into local optimum and greatly enhance precision.
MATHEMATICAL PROBLEMS IN ENGINEERING
(2021)
Article
Computer Science, Artificial Intelligence
Hu Peng, Zhaogan Zeng, Changshou Deng, Zhijian Wu
Summary: Cuckoo search algorithm is effective but can get trapped in local optimum due to unitary search strategy. To overcome this, a multi-strategy serial CS algorithm (MSSCS) is proposed with new learning strategies based on cuckoo's behavior, aiming to enhance performance.
KNOWLEDGE-BASED SYSTEMS
(2021)
Article
Computer Science, Artificial Intelligence
Xiangbo Qi, Zhonghu Yuan, Yan Song
Summary: Integrating heterogeneous biological-inspired strategies and mechanisms into one algorithm can avoid the shortcomings of single algorithm. The proposed integrated cuckoo search optimizer (ICSO) and multi-objective version MOICSO demonstrate the effectiveness of the integrated mechanism and the superior performance of the algorithm.
PEERJ COMPUTER SCIENCE
(2021)
Article
Computer Science, Information Systems
Lukasz Knypinski, Sebastian Kuroczycki, Fausto Pedro Garcia Marquez
Summary: This paper applies the cuckoo search algorithm to optimize the commutation torque ripple in brushless DC motors, successfully reducing the ripple by developing a mathematical model of the BLDC motor and calculating relevant parameters using the finite element method.
Article
Computer Science, Artificial Intelligence
Thanh Cuong-Le, Hoang-Le Minh, Samir Khatir, Magd Abdel Wahab, Minh Thi Tran, Seyedali Mirjalili
Summary: In this paper, a new Cuckoo search algorithm NMS-CS is proposed, which outperforms the original CS in convergence rate and accuracy by modifying the step mechanism. Through analysis of 23 benchmark functions, NMS-CS shows superior performance compared to CS. Furthermore, NMS-CS demonstrates good performance on engineering design problems.
EXPERT SYSTEMS WITH APPLICATIONS
(2021)
Article
Computer Science, Information Systems
Lalit Maurya, Viney Lohchab, Prasant Kumar Mahapatra, Janos Abonyi
Summary: Many vision-based systems suffer from poor levels of contrast and brightness due to inadequate and improper illumination during image acquisition. By using nature-inspired optimization, a balance between contrast and brightness can be achieved in image enhancement, improving image quality.
JOURNAL OF KING SAUD UNIVERSITY-COMPUTER AND INFORMATION SCIENCES
(2022)
Article
Computer Science, Artificial Intelligence
Jafar Gholami, Farhad Mardukhi, Hossam M. Zawbaa
Summary: Meta-heuristic algorithms, such as the crow search algorithm (CSA), have shown promising results in solving optimization problems, but often suffer from issues such as local optima and premature convergence. This paper introduces an improved version, ICSA, which utilizes a new update mechanism to enhance convergence and local search ability. Experimental results demonstrate that ICSA outperforms traditional CSA and other meta-heuristic algorithms in terms of solution accuracy and efficiency.
Article
Computer Science, Artificial Intelligence
Junbo Lian, Guohua Hui
Summary: This paper introduces the Human Evolutionary Optimization Algorithm (HEOA), which is a metaheuristic algorithm inspired by human evolution. The algorithm divides the global search process into two distinct phases and uses unique search strategies. Comparative analysis with other algorithms demonstrates the effectiveness of HEOA in approximating optimal solutions for complex global optimization problems.
EXPERT SYSTEMS WITH APPLICATIONS
(2024)
Article
Computer Science, Artificial Intelligence
Ruyi Dong, Huiling Chen, Ali Asghar Heidari, Hamza Turabieh, Majdi Mafarja, Shengsheng Wang
Summary: The Kernel Search Optimization (KSO) algorithm was proposed to simplify the optimization process by transforming the optimization of nonlinear functions into a linear process. By adopting a local search of the hill-climbing algorithm and simplifying the calculation of kernel parameters, the improved algorithm outperformed the original KSO and some well-known algorithms in terms of accuracy and running time.
KNOWLEDGE-BASED SYSTEMS
(2021)
Article
Computer Science, Hardware & Architecture
Heng Wen, Su Xin Wang, Fu Qiang Lu, Ming Feng, Lei Zhen Wang, Jun Kai Xiong, Ma Cong Si
Summary: This paper introduces a novel metaheuristic optimizer called Colony Search Optimization Algorithm (CSOA), which mimics the social behavior of early humans to enhance algorithm performance and achieves competitive optimization results on multiple tested problems.
JOURNAL OF SUPERCOMPUTING
(2022)
Article
Computer Science, Information Systems
Ali Abdullah Hassan, Salwani Abdullah, Kamal Z. Zamli, Rozilawati Razali
Summary: Daily tasks have been automated by machines and sensors with real-time communication, posing risks and challenges of handling large amounts of data. Combinatorial testing helps reduce risks and improve conformance to specifications. Research on new algorithms and high-order interaction strength generation methods can optimize test suites effectively and enhance performance.
CMC-COMPUTERS MATERIALS & CONTINUA
(2022)
Article
Computer Science, Artificial Intelligence
Mohamed Reda, Mostafa Elhosseini, Amira Haikal, Mahmoud Badawy
Summary: The study proposes a correction to the definition and implementation of the cuckoo search algorithm, and introduces a new algorithm called double exponential cuckoo search. Multiple variants are compared to find the best algorithm that makes the discovery probability adaptive. Through statistical validation and graphical methods, the results demonstrate the superior performance of the proposed algorithm over other variants.
NEURAL COMPUTING & APPLICATIONS
(2021)
Article
Computer Science, Interdisciplinary Applications
Harish Kundra, Wasim Khan, Meenakshi Malik, Kantilal Pitambar Rane, Rahul Neware, Vishal Jain
Summary: The study introduces an integrated quantum-inspired firefly algorithm with cuckoo search (IQFACS) for optimizing solution sets, showing promising results in path planning and optimization problems.
INTERNATIONAL JOURNAL OF MODERN PHYSICS C
(2022)
Article
Computer Science, Artificial Intelligence
Yan Xiong, Jiatang Cheng, Lieping Zhang
Summary: This paper presents a new variant of cuckoo search algorithm, called neighborhood learning-based CS, which aims to solve global optimization problems. The algorithm uses personal best solution for individuals to learn, instead of the best solution found in the entire population, in order to prevent premature convergence. Additionally, each individual is allowed to learn from different learning exemplars in different dimensions, and the exemplar individual is chosen from a predefined neighborhood to maintain population diversity. Experimental results show that the proposed NLCS algorithm demonstrates competitive convergence performance.
INTERNATIONAL JOURNAL OF PATTERN RECOGNITION AND ARTIFICIAL INTELLIGENCE
(2022)
Review
Computer Science, Hardware & Architecture
Farkhana Muchtar, Abdul Hanan Abdullah, Mosleh Al-Adhaileh, Kamal Zuhairi Zamli
JOURNAL OF NETWORK AND COMPUTER APPLICATIONS
(2020)
Article
Computer Science, Artificial Intelligence
Kamal Z. Zamli, Md Abdul Kader, Saiful Azad, Bestoun S. Ahmed
Summary: This paper presents a new variant of the Henry Gas Solubility Optimization (HGSO) Algorithm, called Hybrid HGSO (HHGSO), which has notably improved performance and demonstrated superior results in selected case studies.
NEURAL COMPUTING & APPLICATIONS
(2021)
Article
Computer Science, Information Systems
Fadhl Hujainah, Rohani Binti Abu Bakar, Abdullah B. Nasser, Basheer Al-haimi, Kamal Z. Zamli
Summary: The study introduces a new semi-automated scalable prioritization technique called SRPTackle to address key challenges in requirement prioritization, demonstrating superior accuracy percentages and reduced time consumption in comparison to alternative techniques through experimentation.
INFORMATION AND SOFTWARE TECHNOLOGY
(2021)
Article
Computer Science, Software Engineering
Qusay Sarhan, Bestoun S. Ahmed, Miroslav Bures, Kamal Z. Zamli
Summary: This study reviews 143 research papers on software module clustering to investigate various aspects of clustering methods, applications, processes, algorithms, and evaluation methods. Researchers discuss research gaps and challenges in this field, providing a useful reference for future studies.
IEEE TRANSACTIONS ON SOFTWARE ENGINEERING
(2022)
Article
Computer Science, Information Systems
Md Abdul Kader, Kamal Z. Zamli, Basem Yousef Alkazemi
Summary: The paper introduces a new population-based meta-heuristic algorithm called Emperor Penguin Optimizer (EPO) and discusses its performance and parameter tuning issues. To improve the performance of EPO and address the tuning problem, the paper proposes a fuzzy adaptive variant called Fuzzy Adaptive Emperor Penguin Optimizer (FAEPO), which adjusts parameters adaptively via fuzzy decisions. Experimental results show that FAEPO performs significantly better than EPO and other competing meta-heuristic algorithms in the test suite.
Article
Computer Science, Information Systems
Huma Hanif, Aamer Hanif, Ali Ahsan, Ali Safaa Sadiq, Seyedali Mirjalili, Basem Alkazemi
Summary: This study explores the critical dimensions of organizational structure and schedule management in R&D organizations, finding that decentralized organizational structures are more preferable for timely completion of projects. The proposed framework acts as a supporting mechanism for engineering managers to handle organizational structure and schedule management factors in R&D environments.
Article
Computer Science, Theory & Methods
Hans Christian, Derwin Suhartono, Andry Chowanda, Kamal Z. Zamli
Summary: The growing number of social media users has led to a significant increase in online information. Users' content on social media can predict personality traits without the need for traditional personality tests. Using a multi-model deep learning architecture combined with multiple pre-trained language models as feature extraction methods can enhance prediction accuracy.
JOURNAL OF BIG DATA
(2021)
Article
Computer Science, Information Systems
A. Noraziah, Ainul Azila Che Fauzi, Sharifah Hafizah S. Y. Ahmad Ubaidillah, Basem Alkazemi, Julius Beneoluchi Odili
Summary: Since the invention of computers, large amounts of data have been rapidly generated. Replication is crucial in managing data in distributed database environments, and the proposed algorithm in this paper effectively handles fragmented database synchronous replication to maintain data consistency.
Article
Computer Science, Information Systems
Abdullah B. Nasser, Kamal Z. Zamli, Fadhl Hujainah, Waheed Ali H. M. Ghanem, Abdul-Malik H. Y. Saad, Nayef Abdulwahab Mohammed Alduais
Summary: Over the years, opposition-based Learning (OBL) technique has been proven effective in enhancing the convergence of meta-heuristic algorithms, however, relying on a single OBL technique may not be sufficient. By combining multiple OBL techniques and selecting based on individual performance, search performance can be improved.
Article
Computer Science, Information Systems
Kamal Zuhairi Zamli, Fakhrud Din, Abdullah Nasser, Nazirah Ramli, Noraini Mohamed
Summary: The Flower Pollination Algorithm (FPA) is a relatively new meta-heuristic algorithm inspired by the proliferation role of flowers in plants with the strength of having only one parameter control and dynamic selection of search operators. This paper introduces the Flower Pollination Algorithm Metropolis-Hastings (FPA-MH) which incorporates Metropolis-Hastings criteria from the Simulated Annealing algorithm for dynamic selection of the parameter p(a), showing promising results in test suite generation problems.
JOURNAL OF INFORMATION AND COMMUNICATION TECHNOLOGY-MALAYSIA
(2021)
Article
Computer Science, Theory & Methods
Sharifah Hafizah Sy Ahmad Ubaidillah, A. Noraziah, Basem Alkazemi
Summary: Data replication technique BVAG has been combined with fault tolerance approach CR to improve performance in failure environment, resulting in a reduction of total execution time by 31.65%. Additionally, BVAGCR also significantly reduces the time taken for the most critical phase in BVAGCRTM, the Update (U) phase, by 98.82%.
INTERNATIONAL JOURNAL OF ADVANCED COMPUTER SCIENCE AND APPLICATIONS
(2021)
Article
Computer Science, Information Systems
Ahmed Bashar Fakhri, Saleem Latteef Mohammed, Imran Khan, Ali Safaa Sadiq, Basem Alkazemi, Prashant Pillai, Bong Jun Choi
Summary: Industry 4.0, based on the Internet-of-Things and Big Data, is a disruptive transformation aiming to maximize production efficiency, minimize costs, and meet individual needs, leading to the Fourth Industrial Revolution. This revolution focuses on intelligent industrialization and the combination of material and informationization, emphasizing the importance of data and identity.
CMC-COMPUTERS MATERIALS & CONTINUA
(2021)
Article
Multidisciplinary Sciences
Mohammed Issam Younis, Abdul Rahman A. Alsewari, Ng Yeong Khang, Kamal Z. Zamli
BAGHDAD SCIENCE JOURNAL
(2020)
Article
Computer Science, Information Systems
Md Kamrul Islam, Md Manjur Ahmed, Kamal Zuhairi Zamli, Salman Mehbub
JOURNAL OF INFORMATION AND COMMUNICATION TECHNOLOGY-MALAYSIA
(2020)
Article
Computer Science, Information Systems
Ali Abdullah Hassan, Salwani Abdullah, Kamal Z. Zamli, Rozilawati Razali
Article
Computer Science, Artificial Intelligence
Jin Zhang, Zekang Bian, Shitong Wang
Summary: This study proposes a novel style linear k-nearest neighbor method to extract stylistic features using matrix expressions and improve the generalizability of the predictor through style membership vectors.
APPLIED SOFT COMPUTING
(2024)
Article
Computer Science, Artificial Intelligence
Qifeng Wan, Xuanhua Xu, Jing Han
Summary: In this study, we propose an innovative approach for dimensionality reduction in large-scale group decision-making scenarios that targets linguistic preferences. The method combines TF-IDF feature similarity and information loss entropy to address challenges in decision-making with a large number of decision makers.
APPLIED SOFT COMPUTING
(2024)
Article
Computer Science, Artificial Intelligence
Hegui Zhu, Yuchen Ren, Chong Liu, Xiaoyan Sui, Libo Zhang
Summary: This paper proposes an adversarial attack method based on frequency information, which optimizes the imperceptibility and transferability of adversarial examples in white-box and black-box scenarios respectively. Experimental results validate the superiority of the proposed method and its application in real-world online model evaluation reveals their vulnerability.
APPLIED SOFT COMPUTING
(2024)
Article
Computer Science, Artificial Intelligence
Jing Tang, Xinwang Liu, Weizhong Wang
Summary: This paper proposes a hybrid generalized TODIM approach in the Fine-Kinney framework to evaluate occupational health and safety hazards. The approach integrates CRP, dynamic SIN, and PLTSs to handle opinion interactions and incomplete opinions among decision makers. The efficiency and rationality of the proposed approach are demonstrated through a numerical example, comparison, and sensitivity studies.
APPLIED SOFT COMPUTING
(2024)
Article
Computer Science, Artificial Intelligence
Shigen Shen, Chenpeng Cai, Zhenwei Li, Yizhou Shen, Guowen Wu, Shui Yu
Summary: To address the damage caused by zero-day attacks on SIoT systems, researchers propose a heuristic learning intrusion detection system named DQN-HIDS. By integrating Deep Q-Networks (DQN) into the system, DQN-HIDS gradually improves its ability to identify malicious traffic and reduces resource workloads. Experiments demonstrate the superior performance of DQN-HIDS in terms of workload, delayed sample queue, rewards, and classifier accuracy.
APPLIED SOFT COMPUTING
(2024)
Article
Computer Science, Artificial Intelligence
Song Deng, Qianliang Li, Renjie Dai, Siming Wei, Di Wu, Yi He, Xindong Wu
Summary: In this paper, we propose a Chinese text classification algorithm based on deep active learning for the power system, which addresses the challenge of specialized text classification. By applying a hierarchical confidence strategy, our model achieves higher classification accuracy with fewer labeled training data.
APPLIED SOFT COMPUTING
(2024)
Article
Computer Science, Artificial Intelligence
Kaan Deveci, Onder Guler
Summary: This study proves the lack of robustness in nonlinear IF distance functions for ranking intuitionistic fuzzy sets (IFS) and proposes an alternative ranking method based on hypervolume metric. Additionally, the suggested method is extended as a new multi-criteria decision making method called HEART, which is applied to evaluate Turkey's energy alternatives.
APPLIED SOFT COMPUTING
(2024)
Article
Computer Science, Artificial Intelligence
Fu-Wing Yu, Wai-Tung Ho, Chak-Fung Jeff Wong
Summary: This research aims to enhance the energy management in commercial building air-conditioning systems, specifically focusing on chillers. Ridge regression is found to outperform lasso and elastic net regression when optimized with the appropriate hyperparameter, making it the most suitable method for modeling the system coefficient of performance (SCOP). The key variables that strongly influence SCOP include part load ratios, the operating numbers of chillers and pumps, and the temperatures of chilled water and condenser water. Additionally, July is identified as the month with the highest potential for performance improvement. This study introduces a novel approach that balances feature selection, model accuracy, and optimal tuning of hyperparameters, highlighting the significance of a generic and simplified chiller system model in evaluating energy management opportunities for sustainable operation. The findings from this research can guide future efforts towards more energy-efficient and sustainable operations in commercial buildings.
APPLIED SOFT COMPUTING
(2024)
Article
Computer Science, Artificial Intelligence
Xiaoyan Chen, Yilin Sun, Qiuju Zhang, Xuesong Dai, Shen Tian, Yongxin Guo
Summary: In this study, a method for dynamically non-destructive grasping of thin-skinned fruits is proposed. It utilizes a multi-modal depth fusion convolutional neural network for image processing and segmentation, and combines the evaluation mechanism of optimal grasping stability and the forward-looking non-destructive grasp control algorithm. The proposed method greatly improves the comprehensive performance of grasping delicate fruits using flexible hands.
APPLIED SOFT COMPUTING
(2024)
Article
Computer Science, Artificial Intelligence
Yuxuan Yang, Siyuan Zhou, He Weng, Dongjing Wang, Xin Zhang, Dongjin Yu, Shuiguang Deng
Summary: The study proposes a novel model, POIGDE, which addresses the challenges of data sparsity and elusive motives by solving graph differential equations to capture continuous variation of users' interests. The model learns interest transference dynamics using a time-serial graph and an interval-aware attention mechanism, and applies Siamese learning to directly learn from label representations for predicting future POI visits. The model outperforms state-of-the-art models on real-world datasets, showing potential in the POI recommendation domain.
APPLIED SOFT COMPUTING
(2024)
Article
Computer Science, Artificial Intelligence
S. Karthika, P. Rathika
Summary: The widespread development of monitoring devices in the power system has generated a large amount of power consumption data. Storing and transmitting this data has become a significant challenge. This paper proposes an adaptive data compression algorithm based on the discrete wavelet transform (DWT) for power system applications. It utilizes multi-objective particle swarm optimization (MO-PSO) to select the optimal threshold. The algorithm has been tested and outperforms other existing algorithms.
APPLIED SOFT COMPUTING
(2024)
Article
Computer Science, Artificial Intelligence
Jiaqi Guo, Haiyan Wu, Xiaolei Chen, Weiguo Lin
Summary: In this study, an adaptive SV-Borderline SMOTE-SVM algorithm is proposed to address the challenge of imbalanced data classification. The algorithm maps the data into kernel space using SVM and identifies support vectors, then generates new samples based on the neighbors of these support vectors. Extensive experiments show that this method is more effective than other approaches in imbalanced data classification.
APPLIED SOFT COMPUTING
(2024)
Article
Computer Science, Artificial Intelligence
Qiumei Zheng, Linkang Xu, Fenghua Wang, Yongqi Xu, Chao Lin, Guoqiang Zhang
Summary: This paper proposes a new semantic segmentation network model called HilbertSCNet, which combines the Hilbert curve traversal and the dual pathway idea to design a new spatial computation module to address the problem of loss of information for small targets in high-resolution images. The experiments show that the proposed network performs well in the segmentation of small targets in high-resolution maps such as drone aerial photography.
APPLIED SOFT COMPUTING
(2024)
Article
Computer Science, Artificial Intelligence
Mojtaba Ashour, Amir Mahdiyar
Summary: Analytic Hierarchy Process (AHP) is a widely applied technique in multi-criteria decision-making problems, but the sheer number of AHP methods presents challenges for scholars and practitioners in selecting the most suitable method. This paper reviews articles published between 2010 and 2023 proposing hybrid, improved, or modified AHP methods, classifies them based on their contributions, and provides a comprehensive summary table and roadmap to guide the method selection process.
APPLIED SOFT COMPUTING
(2024)
Review
Computer Science, Artificial Intelligence
Gerardo Humberto Valencia-Rivera, Maria Torcoroma Benavides-Robles, Alonso Vela Morales, Ivan Amaya, Jorge M. Cruz-Duarte, Jose Carlos Ortiz-Bayliss, Juan Gabriel Avina-Cervantes
Summary: Electric power system applications are complex optimization problems. Most literature reviews focus on studying electrical paradigms using different optimization techniques, but there is a lack of review on Metaheuristics (MHs) in these applications. Our work provides an overview of the paradigms underlying such applications and analyzes the most commonly used MHs and their search operators. We also discover a strong synergy between the Renewable Energies paradigm and other paradigms, and a significant interest in Load-Forecasting optimization problems. Based on our findings, we provide helpful recommendations for current challenges and potential research paths to support further development in this field.
APPLIED SOFT COMPUTING
(2024)
