Article
Computer Science, Artificial Intelligence
Tunchan Cura
Summary: This study presents a heuristic approach to portfolio optimization problem using artificial bee colony technique. The results show that the proposed artificial bee colony approach is relatively efficient and effective in solving the problem.
KNOWLEDGE-BASED SYSTEMS
(2021)
Article
Computer Science, Interdisciplinary Applications
Xue Deng, Xiaolei He, Cuirong Huang
Summary: This paper presents a fuzzy random multi-objective portfolio model with different entropy measures and a hybrid algorithm to solve it. By considering the effects of various entropy measures and designing the algorithm, the optimal solution for the portfolio optimization problem is efficiently found.
ENGINEERING COMPUTATIONS
(2022)
Article
Automation & Control Systems
Jiang-Ping Huang, Quan-Ke Pan, Zhong-Hua Miao, Liang Gao
Summary: The study focuses on the DPFSP problem with SDST, proposing three constructive heuristics and a DABC algorithm. The heuristics are based on greedy rule and local search, while the DABC algorithm balances local and global exploration with six composite neighborhood operators. A problem-oriented local search method is introduced to improve the best individual in the population. The proposed methods are shown to be effective compared to existing algorithms in solving the problem.
ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE
(2021)
Article
Computer Science, Artificial Intelligence
Xuanyu Zheng, Changsheng Zhang, Bin Zhang
Summary: This paper presents a novel metaheuristic algorithm based on the Mayfly algorithm to solve the cardinality constrained mean-variance portfolio optimization problem. The experimental results show that the proposed approach achieves competitive performance on datasets of different sizes, demonstrating the feasibility of this approach in solving the problem.
EXPERT SYSTEMS WITH APPLICATIONS
(2023)
Article
Mathematics, Applied
Yulin Deng, Hongfeng Xu, Jie Wu
Summary: This study investigates risk reduction methods of asset securitization using various approaches, including portfolio methods and blockchain technology, to enhance investment security. It proposes an improved ABC algorithm for portfolio optimization and demonstrates its capability to simultaneously optimize multiple features in the investment portfolio, thereby reducing investor errors and improving the balance between investment returns and risks.
JOURNAL OF COMPUTATIONAL AND APPLIED MATHEMATICS
(2021)
Article
Management
Francisco Guijarro, Prodromos E. Tsinaslanidis
Summary: This article proposes a methodology to address the mean-variance optimisation frontier problem with realistic constraints by hybridising a heuristic algorithm with an exact solution approach. The algorithmic framework generates a constrained frontier that actually fulfils the bound and cardinality constraints, unlike other proposals.
JOURNAL OF THE OPERATIONAL RESEARCH SOCIETY
(2021)
Article
Mathematics
Kaixiang Zhu, Lily D. Li, Michael Li
Summary: This paper proposes a conceptual model for the school timetabling problem, considering educators' availabilities, preferences, and expertise. The artificial bee colony algorithm and a virtual search space are used to handle large datasets in an ordinary computing hardware environment. Experimental results show that the proposed approach is more effective than the traditional constraint programming method and can provide more satisfactory solutions.
Review
Automation & Control Systems
Ebubekir Kaya, Beyza Gorkemli, Bahriye Akay, Dervis Karaboga
Summary: The ABC algorithm is a popular optimization algorithm that has been successfully applied to solve real-world problems. This study examines combinatorial optimization approaches based on the ABC algorithm, provides summaries of related studies, and introduces the ABC algorithm-based approaches used. The study also evaluates mechanisms to improve the local search capability of the ABC algorithm and analyzes neighborhood operators, selection schemes, initial populations determination approaches, hybrid approaches, and test instances used in evaluating the performances of ABC algorithms.
ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE
(2022)
Article
Management
David I. Graham, Matthew J. Craven
Summary: Real-world portfolio optimisation problems, often NP-hard, can be efficiently solved using a deterministic method to decompose efficient frontiers into subfrontiers calculated by a quadratic programming algorithm, offering a practical alternative to randomised algorithms. This method can also be applied to other classes of portfolio problems with varying risk measures, and the identified subfrontiers closely correspond to local optima of an evolutionary algorithm's objective function in a case study.
JOURNAL OF THE OPERATIONAL RESEARCH SOCIETY
(2021)
Article
Management
Nathan Phelps, Adam Metzler
Summary: The efficient frontier allows investors to maximize returns for a given risk level. Cardinality constrained efficient frontiers (CCEFs) impose an upper bound on the number of assets in the portfolio. A new algorithm was developed to find CCEFs, which performs well but struggles with certain situations involving bonds and equities. We modified the algorithm to improve CCEFs, although this comes with longer runtimes.
JOURNAL OF THE OPERATIONAL RESEARCH SOCIETY
(2023)
Article
Computer Science, Artificial Intelligence
Maisa Kely de Melo, Rodrigo Tomas Nogueira Cardoso, Tales Argolo Jesus
Summary: This paper proposes a Multiobjective Model Predictive Control strategy for portfolio optimization, which can dynamically adjust the portfolio considering wealth, risk, and real issues imposed by the financial market. The optimization is performed by a multiobjective genetic algorithm, controlling the portfolio composition based on prediction horizon and cardinality. The strategy outperforms other portfolio selection methods and investment funds, even during crisis times like the COVID-19 pandemic.
EXPERT SYSTEMS WITH APPLICATIONS
(2022)
Article
Engineering, Multidisciplinary
Omur Sahin, Bahriye Akay, Dervis Karaboga
Summary: Testing object-oriented software is challenging due to various properties like classes, inheritance, states, behavior, association, and polymorphism. Search-based testing methods like ABC algorithm can automatically generate test cases to optimize coverage goals. Use of archive in ABC algorithm improves convergence and coverage for software testing.
ENGINEERING SCIENCE AND TECHNOLOGY-AN INTERNATIONAL JOURNAL-JESTECH
(2021)
Article
Computer Science, Artificial Intelligence
Gurcan Yavuz, Burhanettin Durmus, Dogan Aydin
Summary: Artificial Bee Colony Algorithm with Distant Savants (ABCDS) is a variant of the Artificial Bee Colony Algorithm designed for solving constrained optimization problems. It improves the search capability by introducing distant learning and competitive local search mechanism. Experimental results show that ABCDS performs better than other algorithms in handling constrained optimization problems.
APPLIED SOFT COMPUTING
(2022)
Article
Computer Science, Artificial Intelligence
Omur Sahin, Bahriye Akay
Summary: Microservices decompose applications into maintainable services and reduce complexity. The study proposes a Discrete Dynamic Artificial Bee Colony with Hyper-Scout algorithm to address issues in RESTful testing generation. Experimental results show the algorithm achieved high performance in multiple problems.
APPLIED SOFT COMPUTING
(2021)
Article
Engineering, Industrial
Yingli Li, Xinyu Li, Liang Gao, Biao Zhang, Quan-Ke Pan, M. Fatih Tasgetiren, Leilei Meng
Summary: This paper presents a mathematical model and a discrete artificial bee colony algorithm for optimizing the distributed hybrid flowshop scheduling problem with sequence dependent setup times. The proposed algorithm utilizes two-level encoding and effective solution update techniques, showing superior performance in experiments.
INTERNATIONAL JOURNAL OF PRODUCTION RESEARCH
(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)