Article
Computer Science, Artificial Intelligence
Yiying Zhang
Summary: NNA is a metaheuristic algorithm with strong global search ability, but its drawbacks include slow convergence and premature convergence when solving complex optimization problems. To overcome these issues, an improved algorithm CCLNNA is introduced, which utilizes competitive learning and chaos theory to enhance optimization performance. Experimental results demonstrate the superiority of CCLNNA in solving complex optimization problems with multimodal properties.
KNOWLEDGE-BASED SYSTEMS
(2021)
Article
Mathematics
Nebojsa Bacanin, Ruxandra Stoean, Miodrag Zivkovic, Aleksandar Petrovic, Tarik A. Rashid, Timea Bezdan
Summary: An enhanced version of the firefly algorithm was proposed in this paper, addressing the drawbacks of the original method through an exploration mechanism and local search strategy. This algorithm was validated for selecting the optimal dropout rate for deep neural network regularization and also applied in image processing tasks.
Article
Computer Science, Information Systems
Nebojsa Bacanin, Khaled Alhazmi, Miodrag Zivkovic, K. Venkatachalam, Timea Bezdan, Jamel Nebhen
Summary: In the field of artificial neural networks, the learning process is a challenging task and finding optimal weights and biases is crucial. Traditional optimization algorithms have limitations in terms of slow convergence and getting stuck in local optima. This study proposes an enhanced brain storm optimization algorithm that balances intensification and diversification to avoid local minima and achieves better results in terms of classification accuracy and convergence speed compared to other state-of-the-art approaches.
CMC-COMPUTERS MATERIALS & CONTINUA
(2022)
Article
Green & Sustainable Science & Technology
Fatih Kilic, Ibrhim Halil Yilmaz, Ozge Kaya
Summary: This study proposed a novel approach using hybrid artificial neural network models to predict monthly radiation, which achieved promising results. The research revealed that indigenous models and widespread models had different feature sets, while the hybrid model performed well in predicting accuracy in both indigenous and widespread regions.
Article
Biodiversity Conservation
Guoli Zhang, Ming Wang, Kai Liu
Summary: This paper compares and analyzes the application of two feedforward neural network models (CNNs and MLPs) in global wildfire susceptibility prediction, and explores the interpretability of the CNNs model. By constructing response variables and monthly wildfire predictors, four MLPs and CNNs architectures were built, and five statistical measures were used to evaluate the prediction performance of the models. The contextual-based CNN-2D model was found to have the highest accuracy, while the MLPs model was more suitable for pixel-based classification, and the performance ranking of the four models was CNN-2D > MLP-1D > MLP-2D > CNN-1D.
ECOLOGICAL INDICATORS
(2021)
Article
Computer Science, Information Systems
Nebojsa Bacanin, Timea Bezdan, K. Venkatachalam, Miodrag Zivkovic, Ivana Strumberger, Mohamed Abouhawwash, Abeer B. Ahmed
Summary: The study proposes an enhanced artificial bee colony optimization algorithm for optimizing connection weights and hidden units of artificial neural networks. Through testing and comparison, the results show that the algorithm outperforms other metaheuristics in terms of accuracy and convergence speed. The improved learning mechanism significantly enhances the convergence speed of the original algorithm and the exploitation capability is enhanced, resulting in significantly better accuracy.
Article
Computer Science, Interdisciplinary Applications
Hongquan Guo, Jian Zhou, Mohammadreza Koopialipoor, Danial Jahed Armaghani, M. M. Tahir
Summary: This study developed a deep neural network (DNN) model to predict flyrock induced by blasting, which showed a significant increase in prediction accuracy compared to an artificial neural network (ANN) model. The DNN model, optimized using the whale optimization algorithm (WOA), successfully minimized flyrock resulting from blasting and provided a suitable pattern for blasting operations in mines.
ENGINEERING WITH COMPUTERS
(2021)
Article
Computer Science, Artificial Intelligence
Saban Gulcu
Summary: The training algorithm is a crucial component of artificial neural networks (ANN) that affects their performance. This article presents a new hybrid algorithm called DA-MLP, which uses the dragonfly algorithm to train feed-forward multilayer neural networks (MLP). The experimental study shows that the DA-MLP algorithm is more efficient than other algorithms.
APPLIED SOFT COMPUTING
(2022)
Article
Computer Science, Theory & Methods
El-Ghazali Talbi
Summary: This article proposes a unified way to describe various optimization algorithms, focusing on common and important search components. This methodology has also been extended to advanced optimization approaches including surrogate-based, multi-objective, and parallel optimization.
ACM COMPUTING SURVEYS
(2021)
Article
Computer Science, Artificial Intelligence
Manuel Lozano, Francisco J. Rodriguez
Summary: This paper presents a method for reconstructing network topology from betweenness centrality values, using an artificial bee colony algorithm and recent update techniques, with satisfactory results shown in extensive experiments.
SWARM AND EVOLUTIONARY COMPUTATION
(2021)
Article
Computer Science, Artificial Intelligence
Essam H. Houssein, Diego Oliva, Emre Celik, Marwa M. Emam, Rania M. Ghoniem
Summary: Feature selection is an optimization problem that aims to simplify and improve the quality of highly dimensional datasets by selecting prominent features and eliminating redundant and irrelevant data to enhance classification accuracy. The Sooty Tern Optimization Algorithm (STOA) and its improved version mSTOA are used to optimize the feature selection problem. However, mSTOA performs better than STOA in terms of convergence to optimal solutions, as validated through experiments and statistical analyses.
EXPERT SYSTEMS WITH APPLICATIONS
(2023)
Article
Computer Science, Artificial Intelligence
Ilker Golcuk, Fehmi Burcin Ozsoydan, Esra Duygu Durmaz
Summary: This paper introduces an improved Arithmetic Optimization Algorithm (AOA) for training artificial neural networks (ANNs) in dynamic environments. The proposed algorithm optimizes the connection weights and biases of the ANN under concept drift, outperforming state-of-the-art metaheuristic optimization algorithms in training ANNs for dynamic classification tasks. The findings demonstrate the potential of the improved AOA for dynamic data-driven applications.
KNOWLEDGE-BASED SYSTEMS
(2023)
Article
Engineering, Civil
Xian Dong, Yadi Wang, Zhan Wang
Summary: This paper presents a particle swarm optimization-convolutional neural network (PSO-CNN) meta-model for the stochastic sensitivity analysis of structural uncertainty. The model improves the training and prediction accuracy of a small-sample CNN by optimizing the initial learning rate and automatically screening the optimal network structure. The global stochastic sensitivity analysis method quantifies the influence of input parameters on model output parameters and analyzes the influence trend of parameter changes on structural response.
Article
Computer Science, Artificial Intelligence
Bahaeddin Turkoglu, Sait Ali Uymaz, Ersin Kaya
Summary: In this study, binary versions of the Artificial Algae Algorithm (AAA) were presented and used to determine the ideal attribute subset for classification processes. Experimental results and statistical tests confirmed the superior performance of the AAA algorithm in increasing classification accuracy compared to other state-of-the-art binary algorithms.
APPLIED SOFT COMPUTING
(2022)
Article
Geochemistry & Geophysics
Xuyang Bai, Shurun Tan
Summary: This article proposes a novel physics-embedded artificial neural network (P-ANN) inversion algorithm for retrieving the vertical distribution of soil moisture and temperature using multichannel passive microwave observations. The P-ANN approach outperforms traditional optimization algorithms and conventional neural network approaches in dealing with layered soil retrieval. The proposed approach holds great potential for remote sensing applications and solving inverse problem challenges.
IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING
(2023)
Article
Computer Science, Artificial Intelligence
Yushuang Hou, Yaping Fu, Kaizhou Gao, Hui Zhang, Ali Sadollah
Summary: Production and distribution are crucial activities in supply chain management, with a focus on improving efficiency and achieving optimal solutions. This study proposes an integrated distributed production and distribution problem with consideration of time windows, utilizing a mixed integer programming model and an enhanced brain storm optimization algorithm to achieve superior performance compared to state-of-the-art optimizers.
EXPERT SYSTEMS WITH APPLICATIONS
(2022)
Article
Computer Science, Artificial Intelligence
Seyed Morteza Razavi, Ali Sadollah, Abobakr Khalil Al-Shamiri
Summary: This paper investigates conductive polymer-based composites with improved electrical conductivity through the addition of different nanoparticles. Various classification techniques including Taguchi method, artificial neural networks (ANNs), and extreme learning machine (ELM) are applied to analyze and predict the electrical conductivity of the composites. The experimental results demonstrate that the addition of CNT, EG, and CB can significantly enhance the electrical conductivity.
NEURAL COMPUTING & APPLICATIONS
(2022)
Article
Computer Science, Artificial Intelligence
Yaping Fu, Yushuang Hou, Zhenghua Chen, Xujin Pu, Kaizhou Gao, Ali Sadollah
Summary: This study proposes an integrated production and distribution optimization problem, utilizing a mixed integer programming model and an enhanced black widow optimization algorithm. The performance and efficiency of the designed method are validated through extensive experiments, showing its superiority over other optimizers. Experimental analysis on sensitive parameters and comparisons with well-known optimizers are conducted to further demonstrate the effectiveness of the proposed method.
SWARM AND EVOLUTIONARY COMPUTATION
(2022)
Article
Engineering, Multidisciplinary
Bahareh Etaati, Amin Abdollahi Dehkordi, Ali Sadollah, Mohammed El-Abd, Mehdi Neshat
Summary: This paper proposes a comparative truss optimization framework using twelve state-of-the-art bio-inspired algorithms to solve large-scale structural optimization problems, and the results show that the marine predators algorithm outperforms other algorithms in terms of convergence speed and the quality of the proposed designs.
MATHEMATICAL PROBLEMS IN ENGINEERING
(2022)
Article
Engineering, Marine
Pegah Ziyaei, Mahdi Khorasanchi, Hassan Sayyaadi, Ali Sadollah
Summary: This study investigated the effect of non-homogeneity in wind farm layouts on the optimization process. The results showed that selecting larger turbines can significantly reduce the Levelized Cost of Energy (LCOE) for the farm, making it a more cost-effective option for developers.
Article
Computer Science, Artificial Intelligence
Dikshit Chauhan, Anupam Yadav
Summary: This article introduces a population-based optimization technique called Artificial Electric Field Algorithm (AEFA) and its binary version. Theoretical and experimental studies show that the proposed binary versions have high efficiency and optimization ability in solving discrete optimization problems.
EVOLUTIONARY INTELLIGENCE
(2023)
Article
Computer Science, Artificial Intelligence
Xiaomeng Ma, Yaping Fu, Kaizhou Gao, Ali Sadollah, Kai Wang
Summary: This paper addresses a multi-center, multi-objective, and stochastic routing and scheduling problem in home health care, aiming to minimize total operation and penalty costs. By developing a multi-objective cooperation evolutionary algorithm and evaluating the quality and feasibility of the obtained solutions using stochastic simulation, the performance and competitiveness of the algorithm are validated.
SWARM AND EVOLUTIONARY COMPUTATION
(2022)
Article
Computer Science, Software Engineering
Geon Hee Lee, Ali Sadollah, Sang Ho Park, Zong Woo Geem
Summary: There has been a development of many metaheuristic optimization algorithms inspired by various natural and artificial phenomena. While each algorithm should be coded for specific optimization problems, there is an increasing interest in developing software that can function generally and effectively. HS-Solver is an open-source software integrated with Microsoft Excel spreadsheet to solve various optimization problems using a music-inspired harmony search algorithm. This paper presents the basic structure of HS-Solver and its functionality in optimization examples.
Article
Computer Science, Artificial Intelligence
Dikshit Chauhan, Anupam Yadav
Summary: This article proposes an adaptive artificial electric field algorithm (iAEFA) which embeds a comprehensive learning strategy into AEFA. The algorithm utilizes a novel adaptive approach for developing a better learning strategy and has shown a stronger potential to discover better candidate solutions. The objective is to develop an efficient optimizer for continuous optimization problems.
Article
Engineering, Electrical & Electronic
Mohammad Nasir, Ali Sadollah, Hassan Barati, Mona Khodabakhshi, Joong Hoon Kim
Summary: This paper addresses the contingency constrained optimal power flow problem based on generation rescheduling, considering the uncertainty of photovoltaic, wind turbine, and plug-in hybrid electric vehicle. Water cycle algorithm has been utilized to optimize the fuel cost, power losses, and system security. The simulations demonstrate the superior performance of the proposed algorithm in improving security for power systems.
IETE JOURNAL OF RESEARCH
(2023)
Article
Computer Science, Information Systems
Dikshit Chauhan, Anupam Yadav
Summary: This article proposes a multilevel hierarchical artificial electric field algorithm with competitive and collaborative strategies (PAEFA) to optimize the performance of population-based optimization algorithms. The algorithm constructs a multilevel structure and implements a collaborative mechanism to enhance the diversity and performance of the population. Extensive experiments demonstrate that PAEFA outperforms state-of-the-art algorithms in terms of accuracy, statistical results, and convergence speed, validating its adaptability and effectiveness.
INFORMATION SCIENCES
(2023)
Article
Multidisciplinary Sciences
Deepika Khurana, Anupam Yadav, Ali Sadollah
Summary: This article proposes a method called multi-objective Neural Network Algorithm to solve multi-objective optimization problems. The proposed method shows good performance in solving difficult multi-objective optimization problems.
Proceedings Paper
Computer Science, Artificial Intelligence
Muhammad Sadeqi, Ali Sadollah, Seyed Morteza Razavi
Summary: This paper focuses on the optimization of a spur gear model by considering multiple design variables and using water cycle and neural network algorithms. The obtained optimization results have been validated and analyzed, showing good agreement with the applied finite element approach.
PROCEEDINGS OF 7TH INTERNATIONAL CONFERENCE ON HARMONY SEARCH, SOFT COMPUTING AND APPLICATIONS (ICHSA 2022)
(2022)
Article
Computer Science, Artificial Intelligence
Indu Bala, Anupam Yadav
Summary: A new niching strategy named "Niching Comprehensive Learning Gravitational Search algorithm" is proposed in this study to solve complex problems with multiple solutions. The algorithm efficiently explores the search space without trapping in local optima and locates all possible global optima. CLGSA algorithm successfully solves multimodal problems and the Reactive Power Dispatch problem with significant accuracy.
EVOLUTIONARY INTELLIGENCE
(2022)
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)
