Article
Management
Pierre Nancel-Penard, Nelson Morales, Fabien Cornillier
Summary: A recursive time aggregation-disaggregation heuristic is proposed to solve large-scale multidimensional and multiperiod precedence-constrained knapsack problems. The method performs well in solving large real-life instances and can obtain near-optimal solutions.
EUROPEAN JOURNAL OF OPERATIONAL RESEARCH
(2022)
Article
Chemistry, Multidisciplinary
Xavier Sanchez-Diaz, Jose Carlos Ortiz-Bayliss, Ivan Amaya, Jorge M. Cruz-Duarte, Santiago Enrique Conant-Pablos, Hugo Terashima-Marin
Summary: The research investigates the application of hyper-heuristics in automatic learning, proposing a feature-independent hyper-heuristic model for solving knapsack problems. The results show that the model performs well under different learning conditions and problem sets.
APPLIED SCIENCES-BASEL
(2021)
Article
Management
Alessandro Baldo, Matteo Boffa, Lorenzo Cascioli, Edoardo Fadda, Chiara Lanza, Arianna Ravera
Summary: This paper introduces a new optimization problem called the Polynomial Robust Knapsack Problem. It extends the Robust Knapsack formulation to consider relations between subsets of items, increasing the complexity of the problem. To solve realistic instances within a reasonable time, two heuristics are proposed - one using machine learning techniques and the other using genetic algorithms. Simulation examples demonstrate the effectiveness of these approaches.
EUROPEAN JOURNAL OF OPERATIONAL RESEARCH
(2023)
Article
Computer Science, Artificial Intelligence
Mohamed Kurdi
Summary: This work proposes a new metaheuristic algorithm called ACONEH for open shop scheduling problem with the goal of improving the exploration capability of ant colony optimization and solving OSSP more effectively. The algorithm utilizes a new heuristic information approach that incorporates randomness, diversity, and improvability. Experimental results show that ACONEH achieves significant improvements in reducing the makespan of OSSP compared to traditional methods.
KNOWLEDGE-BASED SYSTEMS
(2022)
Article
Operations Research & Management Science
Jonatas B. C. Chagas, Markus Wagner
Summary: In this article, we address the Thief Orienteering Problem (ThOP) by combining swarm intelligence with a randomized packing heuristic, achieving significant improvements on almost all 432 benchmarking instances.
OPTIMIZATION LETTERS
(2022)
Article
Engineering, Marine
Meng Yu, Yaqiong Lv, Yuhang Wang, Xiaojing Ji
Summary: This paper addresses the berth allocation problems in container terminal ports by considering real-world operational factors and formulating a model. The proposed PACO algorithm outperforms other commonly used algorithms and is more efficient and effective for the dynamic berth allocation problem.
JOURNAL OF MARINE SCIENCE AND ENGINEERING
(2023)
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
Engineering, Industrial
Liping Zhang, Zhenwei Zhu, Xionghui Zhou
Summary: This paper addresses the die scheduling problem encountered in stamping production lines and proposes an NS-ACO algorithm to optimize die transportation. The algorithm represents individual die transportation tasks as nodes, with combinations of adjacent nodes used as heuristic priority knowledge.
INTERNATIONAL JOURNAL OF PRODUCTION RESEARCH
(2022)
Article
Management
Zequn Wei, Jin-Kao Hao, Jintong Ren, Fred Glover
Summary: This paper presents a responsive strategic oscillation algorithm for the NP-hard disjunctively constrained knapsack problem, which achieves high-quality solutions by employing feasible local search and strategic oscillation search. The algorithm also uses a frequency-based perturbation to escape from local optimal traps. Extensive evaluations on benchmark instances and real-world instances demonstrate the effectiveness of the algorithm.
EUROPEAN JOURNAL OF OPERATIONAL RESEARCH
(2023)
Article
Computer Science, Artificial Intelligence
Zequn Wei, Jin-Kao Hao
Summary: This paper presents an iterated hyperplane search approach for the budgeted maximum coverage problem. The algorithm searches on specific areas identified by cardinality-constrained hyperplanes. It combines three procedures - tabu search, hyperplane search, and perturbation - to ensure diversification of the search. The competitiveness of the algorithm is demonstrated on 30 benchmark instances.
EXPERT SYSTEMS WITH APPLICATIONS
(2023)
Article
Computer Science, Interdisciplinary Applications
Zequn Wei, Jin-Kao Hao
Summary: The paper presents a threshold search based memetic algorithm for solving the DCKP problem, combining the memetic framework with threshold search to find high quality solutions. Extensive computational assessments on benchmark instances demonstrate that the algorithm is highly competitive and effective compared to state-of-the-art methods.
COMPUTERS & OPERATIONS RESEARCH
(2021)
Article
Computer Science, Artificial Intelligence
Jinsi Cai, Peng Wang, Siqing Sun, Huachao Dong
Summary: This paper introduces a new ant colony optimization algorithm called dynamic space reduction ant colony optimization (DSRACO) to solve the capacitated vehicle routing problem. The experimental results show that DSRACO can solve this problem with satisfactory results.
Article
Computer Science, Artificial Intelligence
Jose Garcia, Carlos Maureira
Summary: In this work, a hybrid algorithm incorporating the k-nearest neighbor technique was evaluated to enhance the results of a quantum cuckoo search algorithm for resource allocation. Experimental results demonstrate the significant contribution of the k-nearest neighbor technique to the final solutions, showing that the hybrid algorithm consistently outperforms state-of-the-art algorithms in most analyzed instances.
APPLIED SOFT COMPUTING
(2021)
Article
Computer Science, Artificial Intelligence
Zequn Wei, Jin-Kao Hao
Summary: The algorithm introduced in this study aims to solve the Set-union Knapsack Problem efficiently through its original kernel-based search components and an effective local search procedure. Extensive computational assessments demonstrate its high performance on benchmark instances. Providing access to the algorithm's code aims to facilitate its practical use.
EXPERT SYSTEMS WITH APPLICATIONS
(2021)
Article
Computer Science, Artificial Intelligence
Badr Afify, Andrei Soeanu, Anjali Awasthi
Summary: This paper presents a novel integer programming formulation for the capacitated Facility Location Problem under disruption, namely the Reliable Capacitated Facility Location problem. The proposed solution involves linearization of the model and iterative approach for fortification budget allocation. A case study is used to illustrate the approach and benchmark results are provided.
EXPERT SYSTEMS WITH APPLICATIONS
(2021)
Article
Automation & Control Systems
Mashood Mukhtar, Dhayaa Khudher, Tatiana Kalganova
Summary: This paper presents a novel design of an ambidextrous robot arm with unique features in design, actuation, and control. The arm's efficiency is tested by comparing its performance with a conventional robot arm, showing satisfactory results in power consumption and stability.
IET CONTROL THEORY AND APPLICATIONS
(2021)
Article
Computer Science, Artificial Intelligence
Adam Byerly, Tatiana Kalganova, Ian Dear
Summary: The study demonstrates that a simple convolutional neural network using Homogeneous Vector Capsules (HVCs) performs as well as previous capsule networks on the MNIST dataset, but with fewer parameters, fewer training epochs, and no routing mechanism required.
Article
Chemistry, Analytical
German Sternharz, Jonas Skackauskas, Ayman Elhalwagy, Anthony J. Grichnik, Tatiana Kalganova, Md Nazmul Huda
Summary: This paper introduces a method to compare the functional behaviour of electronic hardware units using a virtual sensor network and a neural network. The method successfully identifies and describes the unexpected behaviour of the test device.
Review
Chemistry, Analytical
Luiz G. Galvao, Maysam Abbod, Tatiana Kalganova, Vasile Palade, Md Nazmul Huda
Summary: Autonomous Vehicles (AVs) have the potential to solve traffic problems, but challenges like accurate perception and detection performance improvement still need to be addressed. Current research focuses on pedestrian and vehicle detection, with Deep Learning techniques showing the best results.
Article
Chemistry, Multidisciplinary
Ayman Elhalwagy, Tatiana Kalganova
Summary: This paper introduces a novel neural network architecture that utilizes LSTM encoder and Capsule decoder to tackle issues in anomaly detection. Experimental results demonstrate the advantages of the proposed architecture in model resilience, training efficiency, and learning multi-variate data consistency.
APPLIED SCIENCES-BASEL
(2022)
Proceedings Paper
Engineering, Electrical & Electronic
Dominic Sanderson, Ben Malin, Tatiana Kalganova, Richard Ott
Summary: In this study, we aim to reduce the computational time and improve model accuracy by using dynamic data reduction techniques on moderately complex datasets, thereby mitigating the environmental impact of deep learning models.
2022 IEEE PHYSICAL ASSURANCE AND INSPECTION OF ELECTRONICS (PAINE)
(2022)
Proceedings Paper
Engineering, Electrical & Electronic
B. Malin, T. Kalganova, J. Danskins, J. R. Gilchrist
Summary: This study evaluates the potential of using near infrared hyperspectral imaging to detect defects in printed circuit boards by comparing the visible differences between various NIR wavelengths and comparing them to X-ray and visible light images. Additionally, a small sample of NIR, visible light, and X-ray PCBA images will be made available to the public.
2022 IEEE PHYSICAL ASSURANCE AND INSPECTION OF ELECTRONICS (PAINE)
(2022)
Article
Computer Science, Information Systems
German Sternharz, Ayman Elhalwagy, Tatiana Kalganova
Summary: Prognostics and Health Monitoring (PHM) of machinery is important for industrial applications. This study proposes a novel methodology that quantifies machine health and calculates a Health Index (HI) before predicting Remaining Useful Life (RUL). Results show that the proposed method outperforms the baseline method, especially with a reduced training set. Additionally, the proposed approach reduces required computing resources.
Proceedings Paper
Computer Science, Artificial Intelligence
Adam Byerly, Tatiana Kalganova, Richard Ott
Summary: This article reviews the methods used in the top 40 highest accuracy models on the ILSVRC 2012 Imagenet validation set. Many of these models utilize transformer-based architectures, although none of them are naive self-attention transformers. Instead, the reviewed works explore different ways to combine the global nature of self-attention with the local nature of fine-grained image features, traditionally the strength of convolutional neural networks.
INTELLIGENT COMPUTING, VOL 2
(2022)
Proceedings Paper
Computer Science, Artificial Intelligence
Jenan Moosa, Wasan Awad, Tatiana Kalganova
Summary: Community Detection is a rapidly growing field with applications in various disciplines. This research aims to develop an improved Label Propagation algorithm that considers the attributes of nodes to achieve fair Homogeneity and high Modularity. It also introduces an adaptive Homogeneity measure and proposes a novel dataset for COVID-19 contact tracing. The proposed algorithm outperformed other algorithms in terms of Modularity and Homogeneity measures.
ICAART: PROCEEDINGS OF THE 14TH INTERNATIONAL CONFERENCE ON AGENTS AND ARTIFICIAL INTELLIGENCE - VOL 3
(2022)
Proceedings Paper
Computer Science, Artificial Intelligence
Adam Byerly, Tatiana Kalganova, Anthony J. Grichnik
Summary: The study presents a dataset of high-resolution images of 13 micro-PCBs, labeled for rotations and perspectives, and experiments show that training a neural network equipped with HVCs and diverse perspectives achieve the highest classification accuracy on micro-PCB data.
INTELLIGENT DECISION TECHNOLOGIES, KES-IDT 2021
(2021)
Article
Computer Science, Information Systems
Adam Byerly, Tatiana Kalganova
Summary: The article introduces a new method of parameterizing and training capsules, called homogeneous vector capsules (HVCs), and finds that modifying a convolutional neural network to use HVCs can improve classification accuracy. The introduction of HVCs enables the use of adaptive gradient descent, reducing the model's dependence on non-adaptive optimizers.
Article
Computer Science, Artificial Intelligence
Guiliang Gong, Jiuqiang Tang, Dan Huang, Qiang Luo, Kaikai Zhu, Ningtao Peng
Summary: This paper proposes a flexible job shop scheduling problem with discrete operation sequence flexibility and designs an improved memetic algorithm to solve it. Experimental results show that the algorithm outperforms other algorithms in terms of performance. The proposed model and algorithm can help production managers obtain optimal scheduling schemes considering operations with or without sequence constraints.
SWARM AND EVOLUTIONARY COMPUTATION
(2024)
Article
Computer Science, Artificial Intelligence
Daniel Molina-Perez, Efren Mezura-Montes, Edgar Alfredo Portilla-Flores, Eduardo Vega-Alvarado, Barbara Calva-Yanez
Summary: This paper presents a new proposal based on two fundamental strategies to improve the performance of the differential evolution algorithm when solving MINLP problems. The proposal considers a set of good fitness-infeasible solutions to explore promising regions and introduces a composite trial vector generation method to enhance combinatorial exploration and convergence capacity.
SWARM AND EVOLUTIONARY COMPUTATION
(2024)
