Maximizing Efficiency: A Comparative Study of SOMA Algorithm Variants and Constraint Handling Methods for Time Delay System Optimization
Authors: 
Roman Senkerik, Tomas Kadavy, Peter Janku, Michal Pluhacek, Hubert Guzowski, Libor Pekar, Radek Matusu, Adam Viktorin, Maciej Smolka, Aleksander Byrski, Zuzana Kominkova OplatkovaAuthors Info & Claims
Roman Senkerik, Tomas Kadavy, Peter Janku, Michal Pluhacek, Hubert Guzowski, Libor Pekar, Radek Matusu, Adam Viktorin, Maciej Smolka, Aleksander Byrski, Zuzana Kominkova OplatkovaAuthors Info & ClaimsAbstract
This paper presents an experimental study that compares four adaptive variants of the self-organizing migrating algorithm (SOMA). Each variant uses three different constraint handling methods for the optimization of a time delay system model. The paper emphasizes the importance of metaheuristic algorithms in control engineering for time-delayed systems to develop more effective and efficient control strategies and precise model identifications.
The study includes a detailed description of the selected variants of the SOMA and the adaptive mechanisms used. A complex workflow of experiments is described, and the results and discussion are presented. The experimental results highlight the effectiveness of the SOMA variants with specific constraint handling methods for time delay system optimization.
Overall, this study contributes to the understanding of the challenges and advantages of using metaheuristic algorithms in control engineering for time delay systems. The results provide valuable insights into the performance of the SOMA variants and can help guide the selection of appropriate constraint handling methods and the adaptive mechanisms of metaheuristics.
References
[1]
Donald Davendra and Ivan Zelinka. 2016. Self-organizing migrating algorithm. New optimization techniques in engineering (2016). Publisher: Springer.
[2]
Jesica de Armas, Eduardo Lalla-Ruiz, Surafel Luleseged Tilahun, and Stefan Voß. 2022. Similarity in metaheuristics: a gentle step towards a comparison methodology. Natural Computing 21, 2 (2022), 265--287.
[3]
Quoc Bao Diep, Ivan Zelinka, Swagatam Das, and Roman Senkerik. 2020. SOMA T3A for Solving the 100-Digit Challenge. In Swarm, Evolutionary, and Memetic Computing and Fuzzy and Neural Computing (Communications in Computer and Information Science), Aleš Zamuda, Swagatam Das, Ponnuthurai Nagaratnam Suganthan, and Bijaya Ketan Panigrahi (Eds.). Springer International Publishing, Cham, 155--165.
[4]
Absalom E Ezugwu, Amit K Shukla, Rahul Nath, Andronicus A Akinyelu, Jeffery O Agushaka, Haruna Chiroma, and Pranab K Muhuri. 2021. Metaheuristics: a comprehensive overview and classification along with bibliometric analysis. Artificial Intelligence Review 54 (2021), 4237--4316.
[5]
E. Fridman. 2014. Introduction to Time-Delay Systems: Analysis and Control. Springer International Publishing.
[6]
Wenjuan Gu, Yongguang Yu, and Wei Hu. 2017. Artificial bee colony algorithm-based parameter estimation of fractional-order chaotic system with time delay. IEEE/CAA Journal of Automatica Sinica 4, 1 (2017), 107--113.
[7]
Hubert Guzowski, Maciej Smołka, Aleksander Byrski, Libor Pekar, Zuzana Kominkova Oplatkova, Roman Senkerik, Radek Matusu, and Frantisek Gazdos. 2022. Effective Parametric Optimization of Heating-Cooling Process with Optimum near the Domain Border. In 2022 IEEE 11th International Conference on Intelligent Systems (IS). IEEE, 1--6.
[8]
Nikolaus Hansen. 2009. Benchmarking a BI-population CMA-ES on the BBOB-2009 function testbed. GECCO (Companion) (07 2009).
[9]
Nikolaus Hansen. 2016. The CMA Evolution Strategy: A Tutorial.
[10]
Shintaro Ikeda and Ryozo Ooka. 2015. Metaheuristic optimization methods for a comprehensive operating schedule of battery, thermal energy storage, and heat source in a building energy system. Applied energy 151 (2015), 192--205.
[11]
Tomas Kadavy, Michal Pluhacek, Roman Senkerik, and Adam Viktorin. 2019. The ensemble of strategies and perturbation parameter in self-organizing migrating algorithm solving CEC 2019 100-digit challenge. In 2019 IEEE Congress on Evolutionary Computation (CEC). IEEE, 372--375.
[12]
Tomas Kadavy, Michal Pluhacek, Adam Viktorin, and Roman Senkerik. 2020. Self-organizing migrating algorithm with clustering-aided migration. In Proceedings of the 2020 genetic and evolutionary computation conference companion. 1441--1447.
[13]
Tomas Kadavy, Michal Pluhacek, Adam Viktorin, and Roman Senkerik. 2021. SOMA-CLP for competition on bound constrained single objective numerical optimization benchmark: a competition entry on bound constrained single objective numerical optimization at the genetic and evolutionary computation conference (GECCO) 2021. In Proceedings of the genetic and evolutionary computation conference companion. 11--12.
[14]
Piotr Kipiński, Hubert Guzowski, Aleksandra Urbańczyk, Maciej Smołka, Marek Kisiel-Dorohinicki, Aleksander Byrski, Zuzana Kominkova Oplatkova, Roman Senkerik, Libor Pekar, Radek Matusu, et al. 2022. Socio-cognitive Optimization of Time-delay Control Problems using Evolutionary Metaheuristics. In 2022 IEEE 11th International Conference on Intelligent Systems (IS). IEEE, 1--7.
[15]
Guo-Han Lin, Jing Zhang, and Zhao-Hua Liu. 2018. Hybrid particle swarm optimization with differential evolution for numerical and engineering optimization. International Journal of Automation and Computing 15, 1 (2018), 103--114.
[16]
Stuart Lloyd. 1982. Least squares quantization in PCM. IEEE transactions on information theory 28, 2 (1982), 129--137.
[17]
Wim Michiels and S.-I Niculescu. 2014. Stability, control and computation or time-delay systems. An eigenvalue based approach. SIAM.
[18]
Daniel Molina, Javier Poyatos, Javier Del Ser, Salvador García, Amir Hussain, and Francisco Herrera. 2020. Comprehensive taxonomies of nature-and bio-inspired optimization: Inspiration versus algorithmic behavior, critical analysis recommendations. Cognitive Computation 12 (2020), 897--939.
[19]
Mateusz Nabywaniec, Hubert Guzowski, Aleksandra Urbańczyk, Maciej Smolka, Marek Kisiel-Dorohinicki, Aleksander Byrski, Zuzana Kominkova Oplatkova, Roman Senkerik, Libor Pekar, Radek Matusu, et al. 2022. Socio-cognitive optimization of time-delay control problems using agent-based metaheuristics. In 2022 IEEE 11th International Conference on Intelligent Systems (IS). IEEE, 1--7.
[20]
John A. Nelder and Roger Mead. 1965. A simplex method for function minimization. Computer Journal 7 (1965), 308--313.
[21]
Libor Pekař, Mengjie Song, Subhransu Padhee, Petr Dostálek, and František Zezulka. 2022. Parameter identification of a delayed infinite-dimensional heat-exchanger process based on relay feedback and root loci analysis. Scientific Reports 12, 1 (03 Jun 2022), 9290.
[22]
Michal Pluhacek, Anezka Kazikova, Tomas Kadavy, Adam Viktorin, and Roman Senkerik. 2021. Explaining SOMA: the relation of stochastic perturbation to population diversity and parameter space coverage. In Proceedings of the Genetic and Evolutionary Computation Conference Companion. 1944--1952.
[23]
Lenka Skanderova. 2023. Self-organizing migrating algorithm: review, improvements and comparison. Artificial Intelligence Review 56, 1 (2023), 101--172.
[24]
Kenneth Sörensen. 2015. Metaheuristics---the metaphor exposed. International Transactions in Operational Research 22, 1 (2015), 3--18. ISBN: 0969-6016 Publisher: Wiley Online Library.
[25]
Guohua Wu, Xin Shen, Haifeng Li, Huangke Chen, Anping Lin, and Ponnuthurai N Suganthan. 2018. Ensemble of differential evolution variants. Information Sciences 423 (2018), 172--186.
[26]
G Zames, NM Ajlouni, NM Ajlouni, NM Ajlouni, JH Holland, WD Hills, and DE Goldberg. 1981. Genetic algorithms in search, optimization and machine learning. Information Technology Journal 3, 1 (1981), 301--302.
Index Terms
- Maximizing Efficiency: A Comparative Study of SOMA Algorithm Variants and Constraint Handling Methods for Time Delay System Optimization
Recommendations
Leveraging Large Language Models for the Generation of Novel Metaheuristic Optimization Algorithms
GECCO '23 Companion: Proceedings of the Companion Conference on Genetic and Evolutionary ComputationIn this paper, we investigate the potential of using Large Language Models (LLMs) such as GPT-4 to generate novel hybrid swarm intelligence optimization algorithms. We use the LLM to identify and decompose six well-performing swarm algorithms for ...
A meta-heuristic proposal for inverse kinematics solution of 7-DOF serial robotic manipulator: quantum behaved particle swarm algorithm
AbstractIn this study, a quantum behaved particle swarm algorithm has used for inverse kinematic solution of a 7-degree-of-freedom serial manipulator and the results have been compared with other swarm techniques such as firefly algorithm (FA), particle ...
A comparative study of Artificial Bee Colony algorithm
Artificial Bee Colony (ABC) algorithm is one of the most recently introduced swarm-based algorithms. ABC simulates the intelligent foraging behaviour of a honeybee swarm. In this work, ABC is used for optimizing a large set of numerical test functions ...
Comments
Information & Contributors
Information
Published In
July 2023
2519 pages
Copyright © 2023 ACM.
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].
Sponsors
Publisher
Association for Computing Machinery
New York, NY, United States
Publication History
Published: 24 July 2023
Check for updates
Author Tags
Qualifiers
- Research-article
Conference
GECCO '23 Companion
Sponsor:
GECCO '23 Companion: Companion Conference on Genetic and Evolutionary Computation
July 15 - 19, 2023
Lisbon, Portugal
Acceptance Rates
Overall Acceptance Rate 1,669 of 4,410 submissions, 38%
Contributors
Other Metrics
Bibliometrics & Citations
Bibliometrics
Article Metrics
- 0Total Citations
- 43Total Downloads
- Downloads (Last 12 months)15
- Downloads (Last 6 weeks)2
Reflects downloads up to 12 Feb 2025
Other Metrics
Citations
View Options
Login options
Check if you have access through your login credentials or your institution to get full access on this article.
Sign in