Modification of Genetic Algorithm Based on Extinction Events and Migration
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Published:2023-04-30
Issue:9
Volume:13
Page:5584
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ISSN:2076-3417
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Container-title:Applied Sciences
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language:en
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Short-container-title:Applied Sciences
Author:
Kieszek Rafał1ORCID, Kachel Stanisław1ORCID, Kozakiewicz Adam1ORCID
Affiliation:
1. Institute of Aviation Technology, Faculty of Mechatronics, Armament and Aerospace, Military University of Technology, 00-908 Warszawa, Poland
Abstract
This article presents a genetic algorithm modification inspired by events related to great extinctions. The main objective of the modification was to minimize the number of objective function solutions until the minimum for the function was established. It was assumed that, within each step, a population should be smaller than that recommended in the applicable literature, the number of iterations should be limited, the solution area should be variable, and a great extinction event should take place following several iterations. Calculations were performed for 10 individuals within a population, 10 iterations, two generations each, with a great extinction event happening once every three iterations. The developed algorithm was presented, capable of indicating the minimum number of Eggholder and Rastrigin functions, with a higher probability than the master algorithm (default “ga” in MATLAB) at the same number of objective function solutions. An algorithm was proposed focusing on minimizing the randomization of the objective function, which may be an alternative to the surrogate model. Typically, the emphasis is on achieving as much accuracy as possible. This article presents a method for minimizing the randomization of the objective function and obtaining the highest possible accuracy. A method is presented which minimizes the disadvantages of the largest computation time and the need to generate many samples for typical genetic algorithms (GAs). Optimization results for the classic GA, GEGA, WOA, SMA, and SSA algorithms for the Eggholder and Rastrigin functions were compared. A modification of the genetic algorithm was made to obtain a global extreme with satisfactory accuracy and a sufficiently high probability, while minimizing the number of samples calculated on the basis of the objective function. The developed methodology was used to fulfill the target function for the turbine disc.
Funder
Military University of Technology in Warsaw
Subject
Fluid Flow and Transfer Processes,Computer Science Applications,Process Chemistry and Technology,General Engineering,Instrumentation,General Materials Science
Reference66 articles.
1. Chen, L., Asteris, P.G., Tsoukalas, M.Z., Armaghani, D.J., Ulrikh, D.V., and Yari, M. (2022). Forecast of Airblast Vibrations Induced by Blasting Using Support Vector Regression Optimized by the Grasshopper Optimization (SVR-GO) Technique. Appl. Sci., 12. 2. Li, G., Li, Y., Chen, H., and Deng, W. (2022). Fractional-Order Controller for Course-Keeping of Underactuated Surface Vessels Based on Frequency Domain Specification and Improved Particle Swarm Optimization Algorithm. Appl. Sci., 12. 3. Szalai, S., Herold, B., Kurhan, D., Németh, A., Sysyn, M., and Fischer, S. (2023). Optimization of 3D Printed Rapid Prototype Deep Drawing Tools for Automotive and Railway Sheet Material Testing. Infrastructures, 8. 4. Ameen, F. (2022). Optimization of the Synthesis of Fungus-Mediated Bi-Metallic Ag-Cu Nanoparticles. Appl. Sci., 12. 5. Dong, L., Qin, L., Xie, X., Zhang, L., and Qin, X. (2023). Collaborative Optimization Method for Multi-Train Energy-Saving Control with Urban Rail Transit Based on DRLDA Algorithm. Appl. Sci., 13.
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