Article
Computer Science, Theory & Methods
Shouyong Jiang, Juan Zou, Shengxiang Yang, Xin Yao
Summary: Evolutionary dynamic multi-objective optimisation (EDMO) is a rapidly growing area that uses evolutionary approaches to solve multi-objective optimisation problems with time-varying changes. After nearly two decades, significant advancements have been made in theoretic research and applications. This article provides a comprehensive survey and taxonomy of existing research on EDMO, as well as highlighting multiple research opportunities for further development.
ACM COMPUTING SURVEYS
(2023)
Article
Mathematics
Cong Wang, Jun He, Yu Chen, Xiufen Zou
Summary: This paper focuses on the importance of studying the binomial crossover function in differential evolution algorithms. It is discovered that using binomial crossover can improve the performance of evolutionary algorithms on certain problems, especially on Deceptive.
Article
Computer Science, Artificial Intelligence
Jialin Liu, Qingquan Zhang, Jiyuan Pei, Hao Tong, Xudong Feng, Feng Wu
Summary: This paper explores the engine calibration process by modeling a real aero-engine calibration problem as a many-objective optimization problem and proposing a fast many-objective evolutionary optimization algorithm. Comparisons with other optimization algorithms show that the fSDE algorithm exhibits better performance in terms of efficiency and quality.
COMPLEX & INTELLIGENT SYSTEMS
(2022)
Article
Computer Science, Information Systems
Masoomeh Mir, Mahdi Yaghoobi, Maryam Khairabadi
Summary: This paper proposes an efficient energy routing approach based on the sleep-wake schedule of nodes in the Internet of Things. By selecting the optimal path, the required energy consumption can be reduced. The proposed method, utilizing the chaos fuzzy grasshopper optimization algorithm, shows better efficiency in terms of remaining energy, network life, and coverage rate compared to the base methods.
MULTIMEDIA TOOLS AND APPLICATIONS
(2023)
Article
Engineering, Marine
Saima Khan, Przemyslaw Grudniewski, Yousaf Shad Muhammad, Adam J. Sobey
Summary: Reducing emissions is becoming increasingly important globally. The regulations of the International Maritime Organisation are pressuring shipping companies to quickly reduce their emissions. Optimizing a ship's route is one solution, with even small reductions leading to significant cost and environmental benefits. This paper compares state-of-the-art algorithms on three case studies and demonstrates the impact of algorithm selection on fuel consumption and expected voyage time. Co-evolutionary algorithms, particularly cMLSGA, show top performance in reducing fuel usage (7.6% on average) and voyage times (8.4% on average) compared to other algorithms.
Article
Computer Science, Artificial Intelligence
Cheng He, Hao Tan, Shihua Huang, Ran Cheng
Summary: Researchers have proposed an effective evolutionary neural architecture search method that achieves state-of-the-art results by using a tailored crossover operator to help offspring architectures inherit from parent architectures.
SWARM AND EVOLUTIONARY COMPUTATION
(2021)
Article
Computer Science, Information Systems
Ahmed A. Ewees, Marwa A. Gaheen, Zaher Mundher Yaseen, Rania M. Ghoniem
Summary: Feature selection is an important phase in data mining, which improves the efficiency of learning models. Comprehensive and greedy algorithms are not suitable for handling a large number of features, and swarm intelligence algorithms are becoming more popular. This paper proposes a new method, called crossover-salp swarm with grasshopper optimization algorithm (cSG), which integrates different algorithms to enhance its performance and flexibility.
Article
Computer Science, Information Systems
Dylan M. Janssen, Wayne Pullan, Alan Wee-Chung Liew
Summary: This paper presents a visualization tool called ECvis that assists in the development of population-based numerical optimization algorithms. The tool provides a simple interface with three modes: Density mode, Statistical mode, and Ranges mode. Through examples, the usefulness of ECvis in optimizing high dimensional functions using differential evolution is demonstrated.
Article
Computer Science, Artificial Intelligence
Jing Liu, Sreenatha Anavatti, Matthew Garratt, Kay Chen Tan, Hussein A. Abbass
Summary: While the concept of swarm intelligence was introduced in the 1980s, the first swarm optimisation algorithm was not introduced until 1992. Analyzing nineteen original swarm optimisation algorithms revealed that while state-of-the-art algorithms are competitive in finding solutions, they are more computationally demanding compared to the original algorithms.
ARTIFICIAL INTELLIGENCE REVIEW
(2022)
Article
Computer Science, Information Systems
Guochun Wang, Ali Asghar Heidari, Mingjing Wang, Fangjun Kuang, Wei Zhu, Huiling Chen
Summary: This paper proposed an enhanced variant of the grasshopper optimization algorithm by incorporating chaos theory to address issues like slow convergence and low solution accuracy, significantly improving global optimization competence and convergence performance. The results highlight the importance of regulating internal factors to enhance the quality of results.
Article
Computer Science, Artificial Intelligence
Zhenglei Wei, Huan Zhou, Fei Cen, Lei Xie, Wenqiang Zhu, Peng Zhang, Qinzhi Hao
Summary: This paper proposes a novel algorithm called Triangle Search Optimization (TSO) for accurately identifying the parameters of photovoltaic models. The algorithm consists of two phases: Triangle Vertex Searching (TVS) and Triangle Edge Searching (TES). Experimental results demonstrate that TSO outperforms state-of-the-art algorithms in terms of convergence accuracy.
Article
Computer Science, Information Systems
Monika Verma, Mini Sreejeth, Madhusudan Singh, Thanikanti Sudhakar Babu, Hassan Haes Alhelou
Summary: This research introduces the concept of chaotic mapping and single stage evolutionary algorithm to enhance the performance of the standard hunting-based optimization algorithm. The modified algorithm shows faster convergence and better balance between exploration and exploitation. The proposed technique outperforms the standard method in benchmark tests and demonstrates its significance in real-world design engineering problems.
Article
Computer Science, Artificial Intelligence
Tianlan Mo, Linjing Wang, Yuliang Wu, Junrong Huang, Weikun Liu, Ruimeng Yang, Xin Zhen
Summary: In this study, a novel multi-classifier fusion framework called CLEER was introduced, which generated diverse base classifiers through evolutionary optimization of random projections. The effectiveness of CLEER was demonstrated through extensive evaluations on multiple datasets and compared to benchmark methods. The results showed that CLEER improved ensemble diversity and achieved more accurate classifications, making it a potential tool for fusing diagnostic or prognostic models in medical decision-making.
EXPERT SYSTEMS WITH APPLICATIONS
(2023)
Article
Multidisciplinary Sciences
Takeshi Nishimura, Isao T. Tokuda, Shigehiro Miyachi, Jacob C. Dunn, Christian T. Herbst, Kazuyoshi Ishimura, Akihisa Kaneko, Yuki Kinoshita, Hiroki Koda, Jaap P. P. Saers, Hirohiko Imai, Tetsuya Matsuda, Ole Naesbye Larsen, Uwe Jurgens, Hideki Hirabayashi, Shozo Kojima, W. Tecumseh Fitch
Summary: Human speech production follows the same acoustic principles as vocal production in other animals but has distinctive features due to simplifications in laryngeal anatomy. The loss of vocal membranes allows human speech to avoid spontaneous nonlinear phenomena and acoustic chaos found in other primate vocalizations, leading to stable and harmonic-rich phonation.
Article
Multidisciplinary Sciences
Natalia Rosetti, Daniela Krohling, Maria Isabel Remis
Summary: Quaternary climate oscillations and human modification of the environment have shaped the distribution and genetic structure of modern species. This study on the South American grasshopper, Dichroplus vittatus, revealed significant differentiation among populations and two major mitochondrial lineages. The results suggest that these populations derived from a single ancestral population and colonized the region after the Last Glacial Maximum.
SCIENTIFIC REPORTS
(2022)
Article
Computer Science, Artificial Intelligence
Akash Saxena
Summary: In this paper, an intelligent harmonic estimator based on an improved version of crow search algorithm is proposed for identification of inter, sub and power harmonics. The proposed algorithm, named ACSA, incorporates acceleration factor driven and opposition-based learning for optimization. Experimental results show that the proposed version is competitive compared to other contemporary algorithms and crow search variants.
EXPERT SYSTEMS WITH APPLICATIONS
(2022)
Article
Computer Science, Artificial Intelligence
Nagraj Deshmukh, Rujuta Vaze, Rajesh Kumar, Akash Saxena
Summary: The Grey Wolf Optimizer (GWO) is a simple and efficient optimization algorithm, but it can struggle with complex problems. To overcome this, researchers propose the Quantum Entanglement enhanced Grey Wolf Optimizer (QEGWO), and benchmark tests show that QEGWO outperforms other variants in terms of estimation accuracy.
EVOLUTIONARY INTELLIGENCE
(2023)
Article
Mathematics
Khalid Abdulaziz Alnowibet, Shalini Shekhawat, Akash Saxena, Karam M. Sallam, Ali Wagdy Mohamed
Summary: Metaheuristics, including bio-inspired ones, have been widely used to solve complex optimization problems. In this paper, a variant of the popular Whale Optimization Algorithm (WOA) called the Augmented Whale Optimization Algorithm (AWOA) is proposed. The AWOA incorporates opposition-based learning and Cauchy mutation operator to improve the exploration and exploitation capabilities of WOA. Experimental results and analyses demonstrate that the proposed AWOA outperforms the original WOA and achieves better optimization performance for both benchmark functions and real-world problems.
Article
Mathematics
Kavita Jain, Muhammed Basheer Jasser, Muzaffar Hamzah, Akash Saxena, Ali Wagdy Mohamed
Summary: This research introduces a novel algorithm called HHO-NN, based on Harris Hawk Optimization, for automatically searching optimal neural network topologies for bidding. The proposed method outperforms other state-of-the-art methods in terms of profit and computational performance, providing precise market information for making valuable bidding decisions.
Article
Computer Science, Artificial Intelligence
Ankit Kumar Sharma, Akash Saxena, D. K. Palwalia
Summary: In recent years, several metaheuristic optimization algorithms have been applied in engineering design problems. Two modifications are proposed to enhance the exploration and exploitation capabilities of the Slime Mould Algorithm. The suitability of the algorithm for designing a Demand Side Management Controller is explored and compared with other algorithms.
EXPERT SYSTEMS WITH APPLICATIONS
(2023)
Article
Chemistry, Multidisciplinary
Rabia Musheer Aziz, Rajul Mahto, Kartik Goel, Aryan Das, Pavan Kumar, Akash Saxena
Summary: Recently, fraudulent practices such as phishing, bribery, and money laundering have become significant challenges to trade security as online trade expands. In this study, a deep learning model was developed using a unique metaheuristic optimization strategy, Optimized Genetic Algorithm-Cuckoo Search (GA-CS), to reliably detect fraudulent transactions. The suggested technique and support vector classification (SVC) models demonstrated the highest accuracy and efficiency in detecting fraudulent behavior on Ethereum.
APPLIED SCIENCES-BASEL
(2023)
Article
Engineering, Multidisciplinary
Varun Sapra, Luxmi Sapra, Akashdeep Bhardwaj, Salil Bharany, Akash Saxena, Faten Khalid Karim, Sara Ghorashi, Ali Wagdy Mohamed
Summary: Despite progress in diagnosis and treatment, cardiovascular diseases remain a leading cause of disease and death worldwide. Artificial intelligence methods can revolutionize cardiology healthcare by improving reliability and accuracy in predicting and responding to CVD. This research focuses on diagnosing coronary artery disease using a deep neural network based on patient clinical data.
ALEXANDRIA ENGINEERING JOURNAL
(2023)
Article
Mathematics
Shoyab Ali, Annapurna Bhargava, Akash Saxena, Pavan Kumar
Summary: Power quality issues can be effectively addressed using filter technologies. The development of hybrid active power filters (HAPF) has been improved due to their ease of control and flexibility. These filters are beneficial for power producers who need a stable and filtered power output. However, designing these filters is challenging and often requires metaheuristic algorithms. This study proposes a new hybrid metaheuristic algorithm (Marine Predator Algorithm and Sine Cosine Algorithm) to optimize the selection of HAPF parameters. The proposed algorithm demonstrates robust results and holds potential for estimation of HAPF parameters, as confirmed by fitness statistical results, boxplots, and numerical analyses.
Article
Mathematics, Applied
Akash Saxena, Adel Fahad Alrasheedi, Khalid Abdulaziz Alnowibet, Ahmad M. Alshamrani, Shalini Shekhawat, Ali Wagdy Mohamed
Summary: This paper proposes a local grey prediction model based on historical data to estimate the market clearing price (MCP). The comparison between traditional and advanced grey models demonstrates the accuracy of the proposed model in predicting the MCP.
Article
Engineering, Electrical & Electronic
Ankit Kumar Sharma, Akash Saxena, Dheeraj Kumar Palwalia, Ramesh C. Bansal
Summary: Demand response (DR) transforms the power market into an interactive one, allowing for the exploration of consumer engagement and progress. The evaluation of DR program implementation is commonly done using consumer baseline load (CBL). This study presents a novel method to compute CBL for residential consumers in a smart grid context, considering the impact of weather on load estimates. Four test cases were examined to validate the proposed method.
ELECTRIC POWER COMPONENTS AND SYSTEMS
(2023)
Article
Energy & Fuels
Pooja Jain, Akash Saxena
Summary: Countries worldwide are restructuring their energy markets in different ways. A flexible simulation platform based on Multi-Agent System (MAS) is proposed in this paper to address electricity market challenges by evaluating existing regulations and testing new market designs. The proposed MAS-EMTS enables agents to make profitable bids and modify generator cost characteristics for maximum profit through real-time monitoring in three phases of estimation, control, and action. The applicability of MAS-EMTS is demonstrated in two power system test cases.
SUSTAINABLE ENERGY GRIDS & NETWORKS
(2023)
Article
Mathematics
Akash Saxena, Ramadan A. Zeineldin, Ali Wagdy Mohamed
Summary: Energy is crucial for the development of a country, and its consumption, production, and transition to green energy are essential for sustainable development. Forecasting technologies, especially grey systems, are gaining attention due to their ability to analyze a limited amount of data. In this study, an optimized grey machine learning model using a polynomial structure was used to predict power generation, consumption, and CO2 emissions, outperforming conventional grey models in terms of accuracy.
Article
Mathematics, Applied
Ankit Kumar Sharma, Ahmad M. Alshamrani, Khalid A. Alnowibet, Adel F. Alrasheedi, Akash Saxena, Ali Wagdy Mohamed
Summary: Demand side management initiatives aim to control energy demand by altering customer demand through techniques such as load shifting and strategic conservation. Research findings suggest that the recommended demand side management solutions can significantly reduce costs while lowering the peak load demand of smart grids.
Article
Mathematics, Applied
Kavita Jain, Akash Saxena, Ahmad M. Alshamrani, Adel Fahad Alrasheedi, Khalid Abdulaziz Alnowibet, Ali Wagdy Mohamed
Summary: This paper discusses the optimal bidding strategy of thermal power generation companies in an electricity market. A modified version of the whale optimization algorithm (AWOA) is proposed and tested, showing its superiority in terms of profit and convergence rate.
Article
Computer Science, Artificial Intelligence
Akash Saxena, Ashutosh Sharma, Shalini Shekhawat
Summary: The focus of power producers has shifted from conventional energy sources to sustainable energy sources due to fossil fuel depletion and carbon emission causing global warming and climate change. Solar cells are the most prominent option for addressing these issues. Accurate estimation of solar cell parameters is crucial for achieving high efficiency in their installation.
EVOLUTIONARY INTELLIGENCE
(2022)
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)
