Article
Computer Science, Artificial Intelligence
Ke-Jing Du, Jian-Yu Li, Hua Wang, Jun Zhang
Summary: Evolutionary multi-objective multi-task optimization is an emerging paradigm for solving multi-objective multi-task optimization problems using evolutionary computation. This paper proposes treating these problems as multi-objective multi-criteria optimization problems and develops an algorithm framework that utilizes the knowledge of all tasks in the same population. The algorithm selects fitness evaluation functions as criteria, guided by a probability-based selection strategy and an adaptive parameter learning method. Extensive experiments show the effectiveness and efficiency of the proposed algorithm. Treating MO-MTOP as MO-MCOP is a potential and promising direction for solving these problems.
COMPLEX & INTELLIGENT SYSTEMS
(2023)
Article
Automation & Control Systems
Yousef Abdi, Mohammad Asadpour, Yousef Seyfari
Summary: In this study, a hybrid micro multi-objective evolutionary algorithm called mu MOSM is proposed to effectively address diversity loss and accelerate the convergence rate in approximating Pareto front solutions.
ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE
(2023)
Article
Thermodynamics
Jan A. Stampfli, Benjamin H. Y. Ong, Donald G. Olsen, Beat Wellig, Rene Hofmann
Summary: Increasing energy efficiency and reducing greenhouse gas emissions are crucial for a sustainable economy. Retrofitting existing multi-period production plants is key to achieving more sustainable production processes in high value-added industries. This study extends an existing evolutionary algorithm to address the multi-objective problem of heat exchanger network retrofit, considering both greenhouse gas emissions and total annual cost. Results from an industrial case study show a 50% reduction in emissions, but a 27% increase in cost compared to single-objective optimization. Selecting different objectives and changes in utility costs and emissions greatly impact the results.
Article
Computer Science, Artificial Intelligence
Yongkuan Yang, Jianchang Liu, Shubin Tan
Summary: Many MOEAs are developed to solve CMOPs, but they encounter low efficiency for steady-state CMOPs. This paper proposes a multi-objective evolutionary algorithm named FACE, which maintains the known feasible solution in the second population and evolves together with the main population. Performance comparisons show the efficiency and scalability of FACE for steady-state CMOPs.
APPLIED SOFT COMPUTING
(2021)
Article
Computer Science, Information Systems
M. Sri Srinivasa Raju, Saykat Dutta, Rammohan Mallipeddi, Kedar Nath Das
Summary: The existence of constrained multi-objective optimization problems (CMOPs) has led researchers to develop constrained multi-objective evolutionary algorithms (CMOEAs). In order to handle CMOPs with discontinuous feasible regions or infeasible barriers, a novel Dual-Population and Multi-Stage based Constrained Multi-objective Evolutionary Algorithm (CMOEA-DPMS) is proposed, along with a new constraint handling technique (CHT) called decomposition based constraint non-dominating sorting (DCDSort) to maintain feasibility, convergence, and diversity.
INFORMATION SCIENCES
(2022)
Article
Computer Science, Interdisciplinary Applications
Xingyi Yao, Wenhua Li, Xiaogang Pan, Rui Wang
Summary: This study focuses on the multi-objective path planning problem and proposes a new solution-encoding method and environmental selection strategy to address the multi-modal minimum path problems. The experiments prove that the proposed method is effective and efficient for multimodal multi-objective path planning.
COMPUTERS & INDUSTRIAL ENGINEERING
(2022)
Article
Computer Science, Information Systems
Haiping Ma, Haoyu Wei, Ye Tian, Ran Cheng, Xingyi Zhang
Summary: Constrained multi-objective optimization problems are challenging to handle due to the complexities of objectives and constraints. To address this issue, a multi-stage evolutionary algorithm is proposed in this paper, which gradually adds constraints and sorts their handling priority based on their impact on the Pareto front. Experimental results demonstrate that the proposed algorithm outperforms state-of-the-art algorithms in dealing with complex constraint problems.
INFORMATION SCIENCES
(2021)
Article
Automation & Control Systems
Yulong Ye, Qiuzhen Lin, Ka-Chun Wong, Jianqiang Li, Zhong Ming, Carlos A. Coello Coello
Summary: This paper proposes a localized decomposition evolutionary algorithm (LDEA) to tackle imbalanced multi-objective optimization problems (MOPs). LDEA assigns a local region for each subproblem using a localized decomposition method and restricts the solution update within the region to maintain diversity. It also speeds up convergence by evolving only the best-associated solution in each subproblem while balancing the population's diversity.
ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE
(2024)
Article
Computer Science, Information Systems
Kai Zhang, Chaonan Shen, Juanjuan He, Gary G. Yen
Summary: The proposed MMO-EvoKnee algorithm incorporates MCDM strategy to efficiently search for a complete set of global knee solutions for MMOPs. It outperforms existing state-of-the-art MMOEAs and provides decision makers with well-converged alternative solutions.
INFORMATION SCIENCES
(2021)
Article
Computer Science, Artificial Intelligence
Jiawei Yuan, Hai-Lin Liu, Zhaoshui He
Summary: Research has shown that a mixture of feasible and infeasible solutions is beneficial for solving constrained multi-objective optimization problems, and the proposed criterion is more effective in identifying valuable infeasible solutions. The algorithm performs well in dealing with complex CMOPs and can successfully handle problems where the initial population is located in infeasible regions below the Pareto fronts.
SWARM AND EVOLUTIONARY COMPUTATION
(2022)
Article
Construction & Building Technology
Gongyue Xu, Zemin Feng, Erkuo Guo, Changwang Cai, Huafeng Ding
Summary: This study established a many-objective optimization model of a new type hydraulic shovel named TriRocker, and proposed an improved many-objective differential evolution algorithm to solve the optimization problem. The most satisfactory solution was chosen through multicriteria decision-making method, resulting in a wonderful design of the TriRocker hydraulic shovel.
AUTOMATION IN CONSTRUCTION
(2022)
Article
Computer Science, Information Systems
Biao Xu, Dunwei Gong, Yong Zhang, Shengxiang Yang, Ling Wang, Zhun Fan, Yonggang Zhang
Summary: In this study, a cooperative co-evolutionary algorithm is proposed to effectively solve multi-objective optimization problems with changing decision variables by dynamically grouping them. The experimental results demonstrate that the presented method outperforms other methods in terms of diversity, convergence, and spread of solutions.
INFORMATION SCIENCES
(2022)
Article
Automation & Control Systems
Hao Sun, Pengfei Chen, Ziyu Hu, Lixin Wei
Summary: The superior performance of evolutionary multitasking algorithms is attributed to the potential synergy between tasks. Current algorithms only transfer individuals from the source to the target task in a unidirectional process. This approach fails to consider the target task's search preference, leading to underutilization of task synergy. We propose a bidirectional knowledge transfer method that takes into account the target task's search preference when finding transferred individuals. Experimental results show that the proposed algorithm outperforms other comparison algorithms in over 30 benchmarks and exhibits considerable convergence efficiency.
Article
Computer Science, Information Systems
Qunjian Chen, Xiaoliang Ma, Yanan Yu, Yiwen Sun, Zexuan Zhu
Summary: This paper proposes a new multi-objective evolutionary multi-task optimization (EMTO) algorithm by introducing cross-dimensional variable search and prediction-based individual search for efficient knowledge transfer. The algorithm is tested on benchmark problems and the experimental results demonstrate its effectiveness and efficiency.
INFORMATION SCIENCES
(2022)
Article
Automation & Control Systems
Sidnei Nascimento, Maury M. Gouvea Jr
Summary: Problems with conflicting objectives in power systems require a multi-objective approach to find tradeoffs between different criteria. An adaptive evolutionary algorithm is introduced to tackle the voltage stability problem, showing enhanced performance compared to other methods, especially as the power system size increases.
INTERNATIONAL JOURNAL OF CONTROL AUTOMATION AND SYSTEMS
(2021)
Article
Computer Science, Artificial Intelligence
Bara'a A. Attea, Wisam A. Hariz, Mayyadah F. Abdulhalim
SWARM AND EVOLUTIONARY COMPUTATION
(2016)
Article
Computer Science, Artificial Intelligence
Bara'a A. Attea, Haidar S. Khoder
SWARM AND EVOLUTIONARY COMPUTATION
(2016)
Article
Computer Science, Artificial Intelligence
Bara'a A. Attea, Qusay Z. Abdullah
Article
Computer Science, Artificial Intelligence
Bara'a A. Attea, Enan A. Khalil, Ahmet Cosar
Article
Telecommunications
Mayyadah F. Abdulhalim, Bara'a A. Attea
WIRELESS PERSONAL COMMUNICATIONS
(2015)
Article
Computer Science, Artificial Intelligence
Amenah H. Abdulateef, Bara'a A. Attea, Ahmed N. Rashid, Mayyadah Al-Ani
APPLIED SOFT COMPUTING
(2018)
Article
Computer Science, Artificial Intelligence
Bara'a A. Attea, Mustafa N. Abbas, Mayyadah Al-Ani, Suat Ozdemir
Article
Computer Science, Information Systems
Enan A. Khalil, Suat Ozdemir, Bara'a A. Attea
IEEE INTERNET OF THINGS JOURNAL
(2019)
Article
Computer Science, Artificial Intelligence
Bara'a A. Attea, Huda M. Rada, Mustafa N. Abbas, Suat Ozdemir
APPLIED SOFT COMPUTING
(2019)
Review
Computer Science, Artificial Intelligence
Bara'a A. Attea, Amenah D. Abbood, Ammar A. Hasan, Clara Pizzuti, Mayyadah Al-Ani, Suat Ozdemir, Rawaa Dawoud Al-Dabbagh
Summary: Current metaheuristic based community detection algorithms tend to reflect a traditional language, lacking depth in reflecting domain knowledge. This paper introduces a new review approach attempting to link heuristic and metaheuristic based community detection methods, proposing two new taxonomies and introducing four new systematic frameworks that integrate both heuristic and metaheuristic algorithms to provide new ideas for designing more effective community detection algorithms.
SWARM AND EVOLUTIONARY COMPUTATION
(2021)
Article
Computer Science, Information Systems
Seema A. Alsaidy, Amenah D. Abbood, Mouayad A. Sahib
Summary: Task scheduling is a significant issue in cloud computing, and this paper proposes an improved initialization method for particle swarm optimization (PSO) using heuristic algorithms. By initializing PSO with longest job to fastest processor (LJFP) and minimum completion time (MCT) algorithms, the performance can be significantly enhanced, compared to traditional PSO and other methods.
JOURNAL OF KING SAUD UNIVERSITY-COMPUTER AND INFORMATION SCIENCES
(2022)
Article
Computer Science, Artificial Intelligence
Amenah D. Abbood, Bara'a A. Attea, Ammar A. Hasan, Richard M. Everson, Clara Pizzuti
Summary: This study takes a new approach to detect the evolution of community structures by decomposing the problem into three essential components: intra-community connections, inter-community connections, and community evolution. Through a multi-objective optimization algorithm and Viterbi algorithm, the proposed model provides temporal smoothness over time for clustering dynamic networks. The results demonstrate the effectiveness of the proposed algorithm in outperforming other algorithms.
ARTIFICIAL INTELLIGENCE REVIEW
(2023)
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)