Article
Computer Science, Artificial Intelligence
Gaoji Sun, Rongqing Han, Libao Deng, Chunlei Li, Guoqing Yang
Summary: Joint operations algorithm (JOA) is a metaheuristic algorithm that utilizes offensive, defensive, and regroup operations to optimize global problems. In order to improve its performance, a hierarchical structure-based variant called HSJOA is proposed by adjusting the execution mechanism of the core operations and redesigning their strategies. Experimental results on real-life optimization problems and test functions demonstrate that HSJOA outperforms both the original JOA and other algorithms, achieving better optimization performance and runtime consumption than L-SHADE and EBOwithCMAR.
SWARM AND EVOLUTIONARY COMPUTATION
(2023)
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
Mohammed Qaraad, Souad Amjad, Nazar K. Hussein, Mostafa A. Elhosseini
Summary: This paper proposes a novel hybrid meta-heuristic algorithm called SSA-FGWO based on the Salp swarm algorithm (SSA) and the Grey Wolf Algorithm (GWO). The experimental results show that SSA-FGWO significantly improves the convergence speed, precision, and global optimization capability compared to the basic SSA, GWO, and other algorithms.
NEURAL COMPUTING & APPLICATIONS
(2022)
Article
Computer Science, Interdisciplinary Applications
Wei Zhang, Ke Pan, Shigang Li, Yagang Wang
Summary: This paper proposes a novel swarm intelligence optimizer called the Special Forces Algorithm (SFA), inspired by human behavior. The algorithm introduces mechanisms such as the speed of light mechanism and loss probability mechanism to fully explore the solution space and enhance the ability to avoid local optima. By simulating the combat tactics of special forces, the algorithm achieves a better balance between exploration and exploitation stages. Experimental results demonstrate the potential and competitiveness of SFA in both benchmark functions and practical engineering problems. SFA achieves good search performance and optimization accuracy based on a balanced exploration and exploitation capability.
MATHEMATICS AND COMPUTERS IN SIMULATION
(2023)
Article
Computer Science, Information Systems
Mohammed Qaraad, Souad Amjad, Nazar K. Hussein, Seyedali Mirjalili, Nadhir Ben Halima, Mostafa A. Elhosseini
Summary: The study introduces a new algorithm called SSALEO that combines the Salp Swarm Algorithm (SSA) with a local escaping operator (LEO) to overcome limitations of SSA. Experimental results show that SSALEO performs competitively and often outperforms other algorithms, including specialized state-of-the-art algorithms.
Article
Computer Science, Artificial Intelligence
Chuan Luo, Sizhao Wang, Tianrui Li, Hongmei Chen, Jiancheng Lv, Zhang Yi
Summary: This article proposes a novel global search method for numerical feature selection, RH-BPSO, based on the hybridization of the rough hypercuboid approach and binary particle swarm optimization (BPSO) algorithm. Parallelization approaches for large-scale datasets are also presented by decomposing and recombining hypercuboid equivalence partition matrix. The experimental results indicate that RH-BPSO outperforms other feature selection algorithms in terms of classification accuracy, the cardinality of the selected feature subset, and execution efficiency. Moreover, the distributed meta-heuristic optimized rough hypercuboid feature selection algorithm, DiRH-BPSO, is significantly faster than its sequential counterpart and can handle large-scale feature selection tasks on distributed-memory multicore clusters.
IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS
(2023)
Article
Automation & Control Systems
Jianhua Xiao, Tao Zhang, Jingguo Du, Xingyi Zhang
Summary: This article proposes a heuristic algorithm, EMRG-HA, to tackle large-scale vehicle routing problems. By utilizing the divide and conquer framework and evolutionary multiobjective route grouping method, the algorithm shows superior performance in solving large-scale CVRPs and outperforms eight existing algorithms in terms of both computational efficiency and solution quality in experimental evaluations.
IEEE TRANSACTIONS ON CYBERNETICS
(2021)
Article
Automation & Control Systems
Jing Bi, Haitao Yuan, Jiahui Zhai, MengChu Zhou, H. Vincent Poor
Summary: This work proposes an improved self-adaptive bat algorithm with genetic operations (SBAGO) that combines genetic algorithm (GA) and bat algorithm (BA) in a highly integrated way. SBAGO utilizes the search information of BA to perform GA's genetic operations, resulting in improved search performance. Experimental results show that SBAGO outperforms other algorithms in various metrics.
IEEE-CAA JOURNAL OF AUTOMATICA SINICA
(2022)
Article
Computer Science, Information Systems
ZhongQuan Jian, GuangYu Zhu
Summary: This paper introduces the concept of affine invariance for meta-heuristic algorithms to verify their dependency on privileged coordinate systems. It is shown that PSO, DE, and OFA algorithms are affine invariant, while GWO, SCA, and BOA algorithms are not. Testing with same random numbers and initial population supports the theoretical analysis results.
INFORMATION SCIENCES
(2021)
Article
Computer Science, Artificial Intelligence
Mohammed Qaraad, Souad Amjad, Nazar K. Hussein, Mostafa A. Elhosseini
Summary: In this study, a salp swarm optimization algorithm (QSSALEO) based on quadratic interpolation and a local escape operator (LEO) is proposed to overcome the limitations of the Salp swarm algorithm (SSA) in dealing with high-dimensional global optimization problems. Experimental results show that QSSALEO outperforms SSA and other population-based algorithms in terms of convergence rate and solution correctness.
NEURAL COMPUTING & APPLICATIONS
(2022)
Article
Computer Science, Artificial Intelligence
Hisham A. Shehadeh
Summary: This paper proposes a new meta-heuristic optimization method called ''Chernobyl Disaster Optimizer (CDO)''. Inspired by the nuclear reactor core explosion of Chernobyl, CDO mimics the process of nuclear radiation while attaching human. The CDO is evaluated by optimizing the ''Congress on Evolutionary Computation (CEC 2017)'' test suite and compared against well-known optimization methods, proving its efficiency.
NEURAL COMPUTING & APPLICATIONS
(2023)
Article
Mathematics
Mohammed Qaraad, Abdussalam Aljadania, Mostafa Elhosseini
Summary: This paper proposes a hybrid approach, CL-SSA, which combines the Competitive Swarm Optimizer (CSO) algorithm with the Salp Swarm algorithm (SSA) to address the slow convergence rate and local optimal solution trapping issues. The CL-SSA algorithm divides the solutions into winners and losers through a pairwise competition mechanism, updates the winners using the SSA algorithm, and allows non-winners to learn from the winners. The performance of the CL-SSA algorithm is evaluated on various benchmark functions and compared with other metaheuristics and advanced algorithms, showing improved exploration, exploitation, and convergence patterns.
Article
Computer Science, Artificial Intelligence
Lalit Kumar, Manish Pandey, Mitul Kumar Ahirwal
Summary: The computational time of swarm optimization algorithms, including Particle Swarm Optimization (PSO), is increased due to the large number of decision variables in complex problems. A new Global Best-Worst Particle Swarm Optimization (GBWPSO) algorithm, combining PSO and Jaya algorithm, is proposed to provide a more parallel version of the algorithm. The proposed algorithm outperforms other parallel PSO versions and Jaya algorithm in terms of computational time and optimal solution.
APPLIED SOFT COMPUTING
(2023)
Article
Computer Science, Information Systems
Adam P. Piotrowski, Jaroslaw J. Napiorkowski, Agnieszka E. Piotrowska
Summary: Hundreds of variants of Swarm Intelligence or Evolutionary Algorithms are proposed each year, but the improvement achieved by these algorithms over Rosenbrock's algorithm is relatively limited, especially for real-world problems.
Article
Computer Science, Information Systems
Shuai Cao, Qian Qian, Yongjun Cao, Wenwei Li, Weixi Huang, Jianan Liang
Summary: This paper presents a novel meta-heuristic optimization algorithm called Piranha Foraging Optimization Algorithm (PFOA) for efficiently solving continuous optimization problems. The algorithm takes inspiration from the flexible and mobile foraging behavior of piranha swarm and divides their foraging behavior into three patterns, simulating these behaviors to construct exploration and exploitation search processes. PFOA uses three strategies to improve population diversity and find better solutions at different stages of the search.
Article
Computer Science, Artificial Intelligence
Gaoji Sun, Yanfei Lan, Ruiqing Zhao
NEURAL COMPUTING & APPLICATIONS
(2019)
Article
Computer Science, Artificial Intelligence
Gaoji Sun, Yanfei Lan, Ruiqing Zhao
Article
Computer Science, Artificial Intelligence
Gaoji Sun, Jin Peng, Ruiqing Zhao
Article
Computer Science, Artificial Intelligence
Gaoji Sun, Ruiqing Zhao
APPLIED INTELLIGENCE
(2014)
Article
Computer Science, Artificial Intelligence
Gaoji Sun, Yankui Liu, Yanfei Lan
JOURNAL OF INTELLIGENT MANUFACTURING
(2011)
Article
Computer Science, Artificial Intelligence
Gaoji Sun, Bai Yang, Zuqiao Yang, Geni Xu
Article
Computer Science, Artificial Intelligence
Gaoji Sun, Geni Xu, Nan Jiang
Article
Mathematics, Applied
Rong Gao, Nana Ma, Gaoji Sun
APPLIED MATHEMATICS AND COMPUTATION
(2019)
Article
Computer Science, Artificial Intelligence
Li-Bao Deng, Chun-Lei Li, Gao-Ji Sun
KNOWLEDGE-BASED SYSTEMS
(2020)
Article
Computer Science, Artificial Intelligence
Gaoji Sun, Chunlei Li, Libao Deng
Summary: The ARSA framework is an adaptive regeneration mechanism based on search space adjustment to alleviate premature convergence or population stagnation issues in the DE algorithm. The framework does not add any parameters that need to be pre-set, and all parameters are adaptive.
NEURAL COMPUTING & APPLICATIONS
(2021)
Article
Computer Science, Information Systems
Libao Deng, Le Song, Gaoji Sun
Summary: The study proposed a multi-objective competitive particle swarm algorithm based on vector angles (VaCSO), which improved convergence and diversity of solutions through competition mechanism and population clustering, thereby enhancing the distribution of solutions in multi-objective optimization problems. Experimental results demonstrated the outstanding performance of VaCSO in optimizing quality.
Article
Computer Science, Artificial Intelligence
Mingfa Zheng, Lisheng Zhang, Yanghe Feng, Linyuan He, Gaoji Sun
EVOLUTIONARY INTELLIGENCE
(2020)
Article
Computer Science, Information Systems
Gaoji Sun, Yiran Wu, Libao Deng, Kai Wang
Proceedings Paper
Computer Science, Artificial Intelligence
Gaoji Sun
2012 IEEE FIFTH INTERNATIONAL CONFERENCE ON ADVANCED COMPUTATIONAL INTELLIGENCE (ICACI)
(2012)
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)