Article
Computer Science, Interdisciplinary Applications
Xinning Li, Hu Wu, Qin Yang, Shuai Tan, Peng Xue, Xianhai Yang
Summary: The study proposed a multistrategy hybrid adaptive whale optimization algorithm (MHWOA) to address the issues with WOA. Through experiments and comparisons, it was found that MHWOA outperformed other algorithms in terms of convergence speed and optimization performance, showing promising applications.
JOURNAL OF COMPUTATIONAL DESIGN AND ENGINEERING
(2022)
Article
Engineering, Chemical
Leila Samandari Masooleh, Jeffrey E. Arbogast, Warren D. Seider, Ulku Oktem, Masoud Soroush
Summary: The algorithm detects community structures in a weighted network by solving a multi-objective optimization problem, adopting the concept of non-dominated sorting to identify Pareto optimal community configurations.
Article
Computer Science, Artificial Intelligence
Subhash V. Pingale, Sanjay R. Sutar
Summary: This paper proposes a hybrid deep model named RWO for detecting network intrusions. The model is trained using a combination of Remora Optimization Algorithm and Whale Optimization Algorithm, and improves detection accuracy through steps such as extracting CNN features and performing feature selection. Experimental results show that the technique achieves superior performance in terms of testing accuracy, precision, recall, and F1 score.
EXPERT SYSTEMS WITH APPLICATIONS
(2022)
Article
Computer Science, Artificial Intelligence
Gian Fritsche, Aurora Pozo
Summary: The cooperative hyper-heuristic HH-CO shows competitive results in solving many-objective optimization problems by utilizing a greedy selection heuristic and cooperative migration procedure, outperforming in 80% of instances. Comparison and analysis of choices made by HH-CO and other models reveal its effectiveness in utilizing MOEAs and distinguishing features that lead to successful outcomes.
EXPERT SYSTEMS WITH APPLICATIONS
(2022)
Article
Engineering, Multidisciplinary
Kouroush Rezvani, Ali Gaffari, Mohammad Reza Ebrahimi Dishabi
Summary: This paper introduces a novel swarm intelligence optimization algorithm called the Bedbug Meta-Heuristic Algorithm (BMHA), which is inspired by the swarming behaviors of bedbugs. The algorithm models the social interaction of bedbugs to perform exploration and exploitation in search for food. Benchmarking tests show that BMHA can improve the initial random population and achieve global optimization, outperforming other well-known algorithms. The algorithm also demonstrates its performance in solving real optimization problems in unknown search spaces.
JOURNAL OF BIONIC ENGINEERING
(2023)
Article
Mathematics
Ruiheng Li, Yi Di, Qiankun Zuo, Hao Tian, Lu Gan
Summary: The transient electromagnetic (TEM) method is a non-contact technique commonly used in mineral resource exploration to identify underground structures. However, the induced polarization (IP) introduces nonlinearity in TEM inversion, making it difficult to predict the geoelectric structure from TEM response signals. In this study, we propose an improved whale optimization algorithm (WOA) with opposition-based learning (OBL) and adaptive weighted factor (AWF) to address the IP effect in TEM inversion. Our tests on layered geoelectric models demonstrate the effectiveness of our approach in reconstructing geoelectric structures and extracting IP information, with superior convergence and accuracy compared to other nonlinear inversion methods.
Article
Engineering, Multidisciplinary
Saeid Raziani, Sajad Ahmadian, Seyed Mohammad Jafar Jalali, Abdolah Chalechale
Summary: In this paper, a novel evolutionary algorithm was developed to train feedforward neural networks (FNNs), which was applied to a classification approach for medical diagnosis problems. The experimental results demonstrated that the proposed method outperformed other competing models in terms of accuracy, AUC, specificity, and sensitivity metrics for the used datasets.
JOURNAL OF BIONIC ENGINEERING
(2022)
Article
Computer Science, Artificial Intelligence
Raghav Prasad Parouha, Pooja Verma
Summary: This paper introduces an advanced hybrid algorithm haDEPSO to solve optimization problems by integrating the advantages of advanced DE and PSO. The efficiency of the algorithm is verified through various test suites, demonstrating its superiority in terms of accuracy and convergence speed.
ARTIFICIAL INTELLIGENCE REVIEW
(2021)
Article
Computer Science, Information Systems
Malik Braik
Summary: In this paper, a new image enhancement method is proposed, which adaptively determines the parameters using a hybrid whale optimization algorithm and chameleon swarm algorithm, and improves the contrast and brightness using bilateral gamma correction. Experimental results show that the proposed method outperforms other methods in multiple evaluation criteria.
CLUSTER COMPUTING-THE JOURNAL OF NETWORKS SOFTWARE TOOLS AND APPLICATIONS
(2022)
Article
Multidisciplinary Sciences
Omnia Magdy, Mohamed Abd Elaziz, Ahmed Elgarayhi, Ahmed A. Ewees, Mohammed Sallah
Summary: This paper presents a machine learning-based technique for interpreting bone scintigraphy images, focusing on feature extraction and introducing a new feature selection method called GJOW. Through extensive experiments, it is shown that the technique has superior predictive effectiveness in bone metastasis detection, with practical implications for early detection and intervention.
SCIENTIFIC REPORTS
(2023)
Article
Multidisciplinary Sciences
Serkan Dereli
Summary: This study introduced a new technique by changing the convergence of the whale optimization algorithm, successfully eliminating the slow convergence and frequent falling to local optimum issues. The new technique provided a 10 million times improvement in solving a complex engineering problem, showcasing the power of the classical whale optimization algorithm with the proposed modification.
ARABIAN JOURNAL FOR SCIENCE AND ENGINEERING
(2022)
Article
Chemistry, Multidisciplinary
Zhen Chen, Kun Zhang, Tommy H. T. Chan, Xiaoke Li, Shunbo Zhao
Summary: This paper proposes a structural damage detection method based on a hybrid whale-chimp optimization algorithm. By introducing the bubble-net hunting mechanism and random search mechanism, the local search ability of the traditional chimp optimization algorithm is improved. Simulation results show that the proposed method has better performance and effectiveness in dealing with multiple damage detection cases.
APPLIED SCIENCES-BASEL
(2022)
Article
Computer Science, Interdisciplinary Applications
Sanjoy Chakraborty, Apu Kumar Saha, Sushmita Sharma, Seyedali Mirjalili, Ratul Chakraborty
Summary: An enhanced Whale Optimization Algorithm (WOAmM) is proposed in this work to overcome premature convergence issues by modifying the mutualism phase, leading to more comprehensive exploration of the search space. The method demonstrates improved performance and superiority over other algorithms in testing.
COMPUTERS & INDUSTRIAL ENGINEERING
(2021)
Article
Engineering, Aerospace
Ya Su, Ying Dai, Yi Liu
Summary: The paper introduces a hybrid algorithm combining the hyper-heuristic whale optimization algorithm and the Gauss pseudospectral method for automatic reentry trajectory optimization without user-specified initial guesses. The proposed algorithm shows promising results in addressing RLV reentry trajectory optimization problems.
AEROSPACE SCIENCE AND TECHNOLOGY
(2021)
Article
Computer Science, Artificial Intelligence
Jianhao Wang, Mohammad Khishe, Mehrdad Kaveh, Hassan Mohammadi
Summary: The Chimp Optimization Algorithm (ChOA) is a new meta-heuristic algorithm inspired by individual intelligence and sexual motivation in chimps, showing better performance among other well-known algorithms. A binary version of ChOA was proposed in this study, emphasizing the importance of transfer functions for binary algorithms. Results indicate that the novel binary approach and V-shaped transfer functions significantly improve the efficiency of BChOAs.
COGNITIVE COMPUTATION
(2021)
Article
Physics, Multidisciplinary
Xiaoyu Shi, Jian Zhang, Xia Jiang, Juan Chen, Wei Hao, Bo Wang
Summary: This study presents a novel framework using offline reinforcement learning to improve energy consumption in road transportation. By leveraging real-world human driving trajectories, the proposed method achieves significant improvements in energy consumption. The offline learning approach demonstrates generalizability across different scenarios.
PHYSICA A-STATISTICAL MECHANICS AND ITS APPLICATIONS
(2024)
Article
Physics, Multidisciplinary
Junhyuk Woo, Soon Ho Kim, Hyeongmo Kim, Kyungreem Han
Summary: Reservoir computing (RC) is a new machine-learning framework that uses an abstract neural network model to process information from complex dynamical systems. This study investigates the neuronal and network dynamics of liquid state machines (LSMs) using numerical simulations and classification tasks. The findings suggest that the computational performance of LSMs is closely related to the dynamic range, with a larger dynamic range resulting in higher performance.
PHYSICA A-STATISTICAL MECHANICS AND ITS APPLICATIONS
(2024)
Article
Physics, Multidisciplinary
Yuwei Yang, Zhuoxuan Li, Jun Chen, Zhiyuan Liu, Jinde Cao
Summary: This paper proposes an extreme learning machine (ELM) algorithm based on residual correction and Tent chaos sequence (TRELM-DROP) for accurate prediction of traffic flow. The algorithm reduces the impact of randomness in traffic flow through the Tent chaos strategy and residual correction method, and avoids weight optimization using the iterative method. A DROP strategy is introduced to improve the algorithm's ability to predict traffic flow under varying conditions.
PHYSICA A-STATISTICAL MECHANICS AND ITS APPLICATIONS
(2024)
Article
Physics, Multidisciplinary
Chengwei Dong, Min Yang, Lian Jia, Zirun Li
Summary: This work presents a novel three-dimensional system with multiple types of coexisting attractors, and investigates its dynamics using various methods. The mechanism of chaos emergence is explored, and the periodic orbits in the system are studied using the variational method. A symbolic coding method is successfully established to classify the short cycles. The flexibility and validity of the system are demonstrated through analogous circuit implementation. Various chaos-based applications are also presented to show the system's feasibility.
PHYSICA A-STATISTICAL MECHANICS AND ITS APPLICATIONS
(2024)
Article
Physics, Multidisciplinary
Viorel Badescu
Summary: This article discusses the maximum work extraction from confined particles energy, considering both reversible and irreversible processes. The results vary for different types of particles and conditions. The concept of exergy cannot be defined for particles that undergo spontaneous creation and annihilation. It is also noted that the Carnot efficiency is not applicable to the conversion of confined thermal radiation into work.
PHYSICA A-STATISTICAL MECHANICS AND ITS APPLICATIONS
(2024)
Article
Physics, Multidisciplinary
P. M. Centres, D. J. Perez-Morelo, R. Guzman, L. Reinaudi, M. C. Gimenez
Summary: In this study, a phenomenological investigation of epidemic spread was conducted using a model of agent diffusion over a square region based on the SIR model. Two possible contagion mechanisms were considered, and it was observed that the number of secondary infections produced by an individual during its infectious period depended on various factors.
PHYSICA A-STATISTICAL MECHANICS AND ITS APPLICATIONS
(2024)
Article
Physics, Multidisciplinary
Zuan Jin, Minghui Ma, Shidong Liang, Hongguang Yao
Summary: This study proposes a differential variable speed limit (DVSL) control strategy considering lane assignment, which sets dynamic speed limits for each lane to attract vehicle lane-changing behaviors before the bottleneck and reduce the impact of traffic capacity drop. Experimental results show that the proposed DVSL control strategy can alleviate traffic congestion and improve efficiency.
PHYSICA A-STATISTICAL MECHANICS AND ITS APPLICATIONS
(2024)
Article
Physics, Multidisciplinary
Matthew Dicks, Andrew Paskaramoorthy, Tim Gebbie
Summary: In this study, we investigate the learning dynamics of a single reinforcement learning optimal execution trading agent when it interacts with an event-driven agent-based financial market model. The results show that the agents with smaller state spaces converge faster and are able to intuitively learn to trade using spread and volume states. The introduction of the learning agent has a robust impact on the moments of the model, except for the Hurst exponent, which decreases, and it can increase the micro-price volatility as trading volumes increase.
PHYSICA A-STATISTICAL MECHANICS AND ITS APPLICATIONS
(2024)
Article
Physics, Multidisciplinary
Zhouzhou Yao, Xianyu Wu, Yang Yang, Ning Li
Summary: This paper developed a cooperative lane-changing decision system based on digital technology and indirect reciprocity. By introducing image scoring and a Q-learning based reinforcement learning algorithm, drivers can continuously evaluate gains and adjust their strategies. The study shows that this decision system can improve driver cooperation and traffic efficiency, achieving over 50% cooperation probability under any connected vehicles penetration and traffic density, and reaching 100% cooperation probability under high penetration and medium to high traffic density.
PHYSICA A-STATISTICAL MECHANICS AND ITS APPLICATIONS
(2024)
Article
Physics, Multidisciplinary
Josephine Nanyondo, Henry Kasumba
Summary: This paper presents a multi-class Aw-Rascle (AR) model with area occupancy expressed in terms of vehicle class proportions. The qualitative properties of the proposed equilibrium velocity and the stability conditions of the model are established. The numerical results show the effect of proportional densities on the flow of vehicle classes, indicating the realism of the proposed model.
PHYSICA A-STATISTICAL MECHANICS AND ITS APPLICATIONS
(2024)
Article
Physics, Multidisciplinary
Oliver Smirnov
Summary: This study proposes a new method for simultaneously estimating the parameters of the 2D Ising model. The method solves a constrained optimization problem, where the objective function is a pseudo-log-likelihood and the constraint is the Hamiltonian of the external field. Monte Carlo simulations were conducted using models of different shapes and sizes to evaluate the performance of the method with and without the Hamiltonian constraint. The results demonstrate that the proposed estimation method yields lower variance across all model shapes and sizes compared to a simple pseudo-maximum likelihood.
PHYSICA A-STATISTICAL MECHANICS AND ITS APPLICATIONS
(2024)
Article
Physics, Multidisciplinary
Przemyslaw Chelminiak
Summary: The study investigates the first-passage properties of a non-linear diffusion equation with diffusivity dependent on the concentration/probability density through a power-law relationship. The survival probability and first-passage time distribution are determined based on the power-law exponent, and both exact and approximate expressions are derived, along with their asymptotic representations. The results pertain to diffusing particles that are either freely or harmonically trapped. The mean first-passage time is finite for the harmonically trapped particle, while it is divergent for the freely diffusing particle.
PHYSICA A-STATISTICAL MECHANICS AND ITS APPLICATIONS
(2024)
Article
Physics, Multidisciplinary
Hidemaro Suwa
Summary: The choice of transition kernel is crucial for the performance of the Markov chain Monte Carlo method. A one-parameter rejection control transition kernel is proposed, and it is shown that the rejection process plays a significant role in determining the sampling efficiency.
PHYSICA A-STATISTICAL MECHANICS AND ITS APPLICATIONS
(2024)
Article
Physics, Multidisciplinary
Xudong Wang, Yao Chen
Summary: This article investigates the joint influence of expanding medium and constant force on particle diffusion. By starting from the Langevin picture and introducing the effect of external force in two different ways, two models with different force terms are obtained. Detailed analysis and derivation yield the Fokker-Planck equations and moments for the two models. The sustained force behaves as a decoupled force, while the intermittent force changes the diffusion behavior with specific effects depending on the expanding rate of the medium.
PHYSICA A-STATISTICAL MECHANICS AND ITS APPLICATIONS
(2024)