Alexandros Tzanetos
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Dr. Tzanetos' main research interests are Evolutionary Computation, Operational Research, and applications in real-world optimization problems. His research focuses on developing intelligent algorithms to solve multi-objective and large-scale optimization applications from fields such as transportation, project management, and energy supply. He is also studying theoretical aspects of Evolutionary Computation, such as the exploration-exploitation trade-off, adaptation of mechanisms, and parameter tuning.
Dr. Tzanetos holds a Financial and Management Engineering Diploma and a PhD in Artificial Intelligence, both from the University of the Aegean, Dept. of Financial and Management Engineering, Greece. He was a Post-Doctorate Fellow at Université de Sherbrooke, Faculty of Engineering, Quebec, Canada, where he worked on a funded project related to the Concrete Delivery Problem.
He is the Section Editor of Artificial Intelligence of the scientific journal Data in Brief (Elsevier).
Article
Tzanetos, A., Dounias, G.
(2024).
Editorial: Introduction to the special issue “Data in Management and Decision Engineering” Data in Brief, 55.
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Thymianis, M., Tzanetos, A., Dounias, G., Koutras, V.
(2023).
Hybridization in nature inspired algorithms as an approach for problems with multiple goals: An application on reliability–redundancy allocation problems Engineering applications of artificial intelligence, 121.
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Tzanetos, A., Blondin, M.
(2023).
Real operational data for the concrete delivery problem Data in Brief, 48.
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Tzanetos, A., Blondin, M.
(2023).
A qualitative systematic review of metaheuristics applied to tension/compression spring design problem: Current situation, recommendations, and research direction Engineering applications of artificial intelligence, 118.
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Tzanetos, A., Blondin, M.
(2023).
Systematic search and mapping review of the concrete delivery problem (CDP): Formulations, objectives, and data.
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Tzanetos, A.
(2023).
Does the Field of Nature-Inspired Computing Contribute to Achieving Lifelike Features? Artificial Life, 29(4), 487-511.
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Konstantinou, C., Tzanetos, A., Dounias, G.
(2022).
Cardinality constrained portfolio optimization with a hybrid scheme combining a Genetic Algorithm and Sonar Inspired Optimization Operational Research, 22(3), 2465-2487.
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Thymianis, M., Tzanetos, A.
(2022).
Is integration of mechanisms a way to enhance a nature-inspired algorithm? Natural Computing.
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Tzanetos, A., Dounias, G.
(2021).
Nature inspired optimization algorithms or simply variations of metaheuristics? Artificial Intelligence Review, 54, 1841-1862.
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Tzanetos, A., Dounias, G.
(2021).
Exploration and exploitation analysis for the sonar inspired optimization algorithm Annals of Mathematics and Artificial Intelligence, 89(8-9), 857-874.
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Tzanetos, A., Dounias, G.
(2020).
Sonar inspired optimization (SIO) in engineering applications Evolving Systems, 11(3), 531-539.
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Tzanetos, A., Vassiliadis, V., Dounias, G.
(2020).
Boosting the performance of hybrid Nature-Inspired algorithms: Application from the financial optimization domain Logic journal of the IGPL (Print), 28(2), 239-247.
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Tzanetos, A., Fister, I., Dounias, G.
(2020).
A comprehensive database of Nature-Inspired Algorithms Data in Brief, 31.
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Ntardas, D., Tzanetos, A., Dounias, G.
(2020).
Resource Leveling Optimization in Construction Projects of High Voltage Substations Using Nature- Inspired Intelligent Evolutionary Algorithms International Journal of Electrical and Computer Engineering, 14(1), 6-13.
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Tzanetos, A., Dounias, G.
(2017).
Nature Inspired Optimization Algorithms Related to Physical Phenomena and Laws of Science: A Survey International journal on artificial intelligence tools, 26(6).
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Doctoral thesis
Tzanetos, A.
(2020).
Νοήμονες υπολογιστικές μέθοδοι εμπνευσμένες από τον φυσικό κόσμο για την βελτιστοποίηση συστημάτων: αλγόριθμος βελτιστοποίησης εμπνευσμένης από τον ηχοεντοπισμό
(Doctoral thesis, Chios:
Polytechnic School, Department of Engineering, Economics and Administration).
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Book chapter
Karampotsis, E., Boulas, K., Georgaki, N., Tzanetos, A., Dounias, G., Papadopoulos, C.
(2023).
Neural Networks Based Throughput Estimation of Short Production Lines Without Intermediate Buffers.
In:
Ioannis K. Hatzilygeroudis, George A. Tsihrintzis & Lakhmi C. Jain
(Ed.),
Fusion of Machine Learning Paradigms:
Theory and Applications
(pp. 175 -198).
Cham: Springer
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Berraies, A., Tzanetos, A., Blondin, M.
(2023).
Machine Learning to Facilitate the Integration of Renewable Energies into the Grid.
In:
M. Fathi, E. Zio & P. M. Pardalos
(Ed.),
Handbook of Smart Energy Systems
(pp. 1 -23).
Cham: Springer
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Tzanetos, A., Dounias, G.
(2020).
A Comprehensive Survey on the Applications of Swarm Intelligence and Bio-Inspired Evolutionary Strategies.
In:
George A. Tsihrintzis & Lakhmi C. Jain
(Ed.),
Machine Learning Paradigms:
Advances in Deep Learning-based Technological Applications
(pp. 337 -378).
Cham: Springer
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Conference paper
Tzanetos, A., Kůdela, J.
(2024).
Working on the Structural Components of Evolutionary Approaches.
16th International Joint Conference on Computational Intelligence ECTA, Porto, Portugal, 20-22 November 2024.
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Küdela, J., Juříček, M., Parǎk, R., Tzanetos, A., Matoušek, R.
(2024).
Benchmarking Derivative-Free Global Optimization Methods on Variable Dimension Robotics Problems.
13th IEEE Congress on Evolutionary Computation, CEC 2024 Yokohama 30 June 2024 through 5 July 2024.
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Thymianis, M., Tzanetos, A., Osaba, E., Dounias, G., Del Ser, J.
(2022).
Electric Vehicle Routing Problem: Literature Review, Instances and Results with a Novel Ant Colony Optimization Method.
2022 IEEE Congress on Evolutionary Computation (CEC), Padua, Italy, 18-23 July 2022.
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Tzanetos, A., Dounias, G.
(2020).
Sonar Inspired Optimization in Energy Problems Related to Load and Emission Dispatch.
Cham:
Springer, 13th International Conference, LION 13, Chania, Crete, Greece, May 27–31, 2019.
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Ntardas, D., Tzanetos, A., Dounias, G.
(2020).
Intelligent Nature-Inspired Approaches for Optimal Resource Levelling in a High Voltage Alternating Current Submarine Link Terminal Station Project.
11th International Conference on Information, Intelligence, Systems and Applications, IISA 2020, 15-17 July 2020.
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Tzanetos, A., Dounias, G.
(2020).
Sonar inspired optimization based feature selection.
11th International Conf erence on Artificial Intelligence, SETN 2020, 2-4 September 2020.
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Boulas, K., Tzanetos, A., Dounias, G.
(2018).
Acquisition of approximate throughput formulas for serial production lines with parallel machines using intelligent techniques.
10th Hellenic Conference on Artificial Intelligence, SETN 2018, 9-12 July 2018.
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Tzanetos, A., Kyriklidis, C., Papamichail, A., Dimoulakis, A., Dounias, G.
(2018).
A nature inspired metaheuristic for optimal leveling of resources in project management.
10th Hellenic Conference on Artificial Intelligence, SETN 2018, 9-12 July 2018.
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Karampotsis, E., Boulas, K., Tzanetos, A., Androvitsaneas, V., Gonos, I., Dounias, G., Stathopulos, I.
(2017).
Computational intelligence techniques for modelling the critical flashover voltage of insulators: From accuracy to comprehensibility.
30th International Conference on Industrial Engineering and Other Applications of Applied Intelligent Systems, IEA/AIE 2017, Arras, France, June 27-30, 2017.
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Tzanetos, A., Dounias, G.
(2017).
A new metaheuristic method for optimization: Sonar inspired optimization.
Cham:
Springer, 18th International Conference, EANN 2017, Athens, Greece, August 25–27, 2017.
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Tzanetos, A., Vassiliadis, V., Dounias, G.
(2017).
A novel hybrid nature-inspired scheme for solving a financial optimization problem.
Cham:
Springer, 12th International Conference, HAIS 2017, La Rioja, Spain, June 21-23, 2017.
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Vassiliadis, V., Dounias, G., Tzanetos, A.
(2012).
Parameter tuning of hybrid nature-inspired intelligent metaheuristics for solving financial portfolio optimization problems.
Berlin:
Springer, 7th Hellenic Conference on AI, SETN 2012, Lamia, Greece, May 28-31, 2012.
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Report
Tzanetos, A., Dounias, G.
(2019).
An application-based taxonomy of Nature Inspired Intelligent algorithms.
Chios:
Management and Decision Engineering Laboratory (MDE-Lab)
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