Article
Green & Sustainable Science & Technology
Iman Ahmadianfar, Ali Kheyrandish, Mehdi Jamei, Bahram Gharabaghi
Summary: An adaptive differential evolution with particle swarm optimization (A-DEPSO) algorithm is developed to derive optimal operating rules for multi-reservoir systems in hydropower generation, showing improved performance compared to other well-known optimizers in the literature.
Article
Automation & Control Systems
Adam P. Piotrowski, Jaroslaw J. Napiorkowski, Agnieszka E. Piotrowska
Summary: This paper compares Particle Swarm Optimization and Differential Evolution, two landmark metaheuristics, and finds that the performance of Differential Evolution algorithms is clearly better than Particle Swarm Optimization ones. Despite being more commonly used in the literature, Particle Swarm Optimization algorithms are outperformed by Differential Evolution on single-objective numerical benchmarks and real-world problems. Therefore, there is a need to reconsider the algorithmic philosophy of Particle Swarm Optimization variants to enhance their competitiveness.
ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE
(2023)
Article
Chemistry, Multidisciplinary
Milos Sedak, Bozidar Rosic
Summary: This research addresses the constrained multi-objective nonlinear optimization problem of planetary gearboxes using a hybrid metaheuristic algorithm. The proposed algorithm successfully obtains solutions of the non-convex Pareto set for optimizing weight, efficiency, and preventing premature gear failure. Compared to other well-known algorithms, it shows improved optimization performance in obtaining Pareto solutions.
APPLIED SCIENCES-BASEL
(2021)
Article
Chemistry, Multidisciplinary
Shir Li Wang, Sarah Hazwani Adnan, Haidi Ibrahim, Theam Foo Ng, Parvathy Rajendran
Summary: Evolutionary computation algorithms and swarm intelligence are widely used for global optimization problems. This paper proposes a hybrid algorithm called FIPSaDE, which combines fully informed particle swarm with self-adaptive differential evolution, to enhance the solving capability.
APPLIED SCIENCES-BASEL
(2022)
Article
Computer Science, Artificial Intelligence
Vibhu Trivedi, Manojkumar Ramteke
Summary: A new hybrid variant of multi-objective differential evolution algorithm is developed in this study, which combines the abilities of DE/rand/1 strategy and adaptive social evolution algorithm to improve convergence speed and effectiveness. The algorithm outperforms other established algorithms in solving computationally intensive multi-objective optimization problems, showing better convergence with a relatively simple structure and no additional computational cost needed.
APPLIED SOFT COMPUTING
(2021)
Article
Computer Science, Interdisciplinary Applications
Xu Yang, Hongru Li
Summary: This study proposes a multi-sample learning particle swarm optimization algorithm to overcome the drawbacks of traditional PSO. It uses a multi-sample selecting strategy and an adaptive sample crossover strategy to select proper learning samples for the population. Experimental results show that the proposed algorithm outperforms other competitive algorithms and meta-heuristics in most functions.
MATHEMATICS AND COMPUTERS IN SIMULATION
(2023)
Article
Computer Science, Artificial Intelligence
Ali Fallahi, Erfan Amani Bani, Seyed Taghi Akhavan Niaki
Summary: The study presents a new multiproduct economic order quantity inventory model for reusable products in inventory systems, aiming to minimize the total cost of the system. The model takes into account operational constraints such as budget, warehouse space, and holding cost. Two new variants of differential evolution (DE) and particle swarm optimization (PSO) algorithms, called DEQL and PSOQL, are introduced to solve the nonlinear model. The algorithms' performance is improved by using reinforcement learning-based parameter adaption.
EXPERT SYSTEMS WITH APPLICATIONS
(2022)
Article
Agriculture, Multidisciplinary
Voravee Punyakum, Kanchana Sethanan, Krisanarach Nitisiri, Rapeepan Pitakaso, Mitsuo Gen
Summary: This research proposed an optimization method called HDEPSO, based on Differential Evolution and Particle Swarm Optimization, for solving workforce scheduling and routing problems in the field service operation of a sugarcane mill company. The results showed that the HDEPSO method outperformed mixed integer programing, as well as the DE and PSO methods, especially for larger-size problems.
COMPUTERS AND ELECTRONICS IN AGRICULTURE
(2022)
Article
Computer Science, Artificial Intelligence
Adam P. Piotrowski, Agnieszka E. Piotrowska
Summary: The paper surveys the rapid publications of DE and PSO applications in 2020 related to COVID-19, and finds that these methods are mainly used for calibration of epidemiological models and image-based classification, with scarce methodological details reported, and choices may not always be appropriate. Research from the past two decades is overlooked, with the main factors influencing choice being citation numbers and code availability in various programming languages.
ARTIFICIAL INTELLIGENCE REVIEW
(2022)
Article
Computer Science, Artificial Intelligence
Cian Steenkamp, Andries P. Engelbrecht
Summary: The scalability of the MGPSO algorithm for many objective optimization problems was investigated in this study. The algorithm demonstrated competitive performance across many objectives compared to other state-of-the-art algorithms, without needing specialized modifications. The use of multiple subswarms and guides in the algorithm helps balance and promote solution accuracy and diversity during the search process.
SWARM AND EVOLUTIONARY COMPUTATION
(2021)
Article
Computer Science, Artificial Intelligence
Diana Cristina Valencia-Rodriguez, Carlos A. Coello Coello
Summary: Particle Swarm Optimization (PSO) is a bio-inspired metaheuristic algorithm that utilizes information exchange between particles to explore the search space. This study focuses on the influence of the number of connections among particles in Multi-Objective Particle Swarm Optimizers (MOPSOs) using random regular graphs as the swarm topology. Experimental results indicate that a higher connection degree can lead to algorithm instability in various problems, and MOPSOs with the same connection degree exhibit similar behavior.
SWARM AND EVOLUTIONARY COMPUTATION
(2023)
Article
Automation & Control Systems
Huan Liu, Junqi Zhang, MengChu Zhou
Summary: This paper proposes an adaptive particle swarm optimizer that combines hierarchical learning with variable population to enhance the performance of the PSO algorithm. By introducing a heap-based hierarchy and adjusting the particle's level based on its current fitness, as well as eliminating redundant particles based on the population's evolution state, the swarm's exploratory and exploitative capabilities are improved.
IEEE TRANSACTIONS ON SYSTEMS MAN CYBERNETICS-SYSTEMS
(2023)
Article
Computer Science, Artificial Intelligence
Meijin Lin, Zhenyu Wang, Weijia Zheng
Summary: This paper proposes a hybrid particle swarm-differential evolution algorithm (HPSDE) to address the shortcomings of premature and slow convergence in traditional differential evolution. The HPSDE algorithm improves optimization performance through a modified particle-swarm mutation strategy and enhanced control parameter adaptation. It also increases population diversity with DE/rand-to-rand/1 mutation strategy and combines both strategies in a random mutation framework for improved convergence and stability.
Article
Automation & Control Systems
Liangliang Zhang, Sung-Kwun Oh, Witold Pedrycz, Bo Yang, Lin Wang
Summary: In this study, a novel promotive particle swarm optimizer with double hierarchical structures is proposed. The method utilizes successful mechanisms from social and biological systems to ensure fair competition among particles. Experimental results demonstrate that the proposed method improves accuracy and convergence speed, particularly in solving complex problems.
IEEE TRANSACTIONS ON CYBERNETICS
(2022)
Article
Energy & Fuels
Chuan Wang, Minyi Xu, Qinjin Zhang, Jinhong Feng, Ruizheng Jiang, Yi Wei, Yancheng Liu
Summary: This paper proposed a self-adaptive particle swarm optimization differential evolution (SaPSODE) algorithm to better identify the parameters of lithium-ion batteries. Experimental results demonstrated the effectiveness of this method in SOC estimation compared to other methods.
JOURNAL OF ENERGY STORAGE
(2021)
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)