Optimizing Swarm Intelligence: A Comprehensive Analysis of Mutation-Based Enhancements
Abstract
Swarm Intelligence (SI) represents an optimization approach inspired by the collective behavior observed in swarms during the search for food. Well-established SI methods, such as Ant Colony Optimization (ACO), Particle Swarm Optimization (PSO), and Artificial Bee Colony (ABC), are complemented by newer methodologies like Cat Swarm Optimization (CSO) and Grasshopper Optimization Algorithm (GOA). Typically, exploration techniques in SI are more effective than exploitation techniques. To enhance exploration capabilities, this research employs a modification technique based on mutation, chosen for its strong exploratory attributes and low complexity. This study introduces 16 modifications by combining four frameworks with four operators. Each modification is paired with the fundamental methods for comprehensive testing. The experimental phase encompasses five benchmark functions of varying dimensions, resulting in 8,000 experiments. Three analytical assessments were conducted based on these results. The initial analysis reveals that the mutation modification has the most substantial impact on the basic ACO method. The second analysis indicates that mutation modification significantly influences the objective function in scenarios with large dimensions. The concluding analysis highlights the paramount influence of the modification incorporating the random parameter mutation framework, whereas the mutation operator modification shows comparatively less significant results. A detailed impact assessment shows that Modification 2B achieved the highest number of positive results, succeeding in 69 out of 100 tests, while 2D modifications yielded the smallest sum and average values. The influence of different frameworks and operators was further analyzed, revealing that frameworks have a more pronounced impact on performance than operators. Framework number 2, in particular, demonstrated the most significant effect on improving average impact values.References
Mykel J. Konchenderfer and T. A. Wheeler, Algorithms For Optimization. London: MIT Press, 2019.
A. Naik and S. C. Satapathy, “A comparative study of social group optimization with a few recent optimization algorithms,” Complex & Intelligent Systems, vol. 7, no. 1, pp. 249–295, 2021, doi: 10.1007/s40747-020-00189-6.
R. Hinterding, H. Gielewski, and T. Peachey, “The Nature of Mutation in Genetic Algorithms.,” Proceedings of the Sixth International Conference on Genetic Algorithms, pp. 65–72, 1995, [Online]. Available: http://pdf.aminer.org/000/310/686/the_nature_of_mutation_in_genetic_algorithms.pdf
M. Sharma and J. K. Chhabra, “Sustainable automatic data clustering using hybrid PSO algorithm with mutation,” Sustainable Computing: Informatics and Systems, vol. 23, pp. 144–157, Sep. 2019, doi: 10.1016/j.suscom.2019.07.009.
S. Rani, B. Suri, and R. Goyal, “On the effectiveness of using elitist genetic algorithm in mutation testing,” Symmetry (Basel), vol. 11, no. 9, 2019, doi: 10.3390/sym11091145.
A. Hassanat, K. Almohammadi, E. Alkafaween, E. Abunawas, A. Hammouri, and V. B. S. Prasath, “Choosing mutation and crossover ratios for genetic algorithms-a review with a new dynamic approach,” Information (Switzerland), vol. 10, no. 12, 2019, doi: 10.3390/info10120390.
C. Audet and W. Hare, “Genetic Algorithms,” Springer Series in Operations Research and Financial Engineering, pp. 57–73, 2017, doi: 10.1007/978-3-319-68913-5_4.
T. M. Shami, A. A. El-Saleh, M. Alswaitti, Q. Al-Tashi, M. A. Summakieh, and S. Mirjalili, “Particle Swarm Optimization: A Comprehensive Survey,” IEEE Access, vol. 10, pp. 10031–10061, 2022, doi: 10.1109/ACCESS.2022.3142859.
E. H. Houssein, A. G. Gad, K. Hussain, and P. N. Suganthan, “Major Advances in Particle Swarm Optimization: Theory, Analysis, and Application,” Swarm Evol Comput, vol. 63, Jun. 2021, doi: 10.1016/j.swevo.2021.100868.
X. Wang, P. Henshaw, and D. S. K. Ting, “Exergoeconomic analysis for a thermoelectric generator using mutation particle swarm optimization (M-PSO),” Appl Energy, vol. 294, Jul. 2021, doi: 10.1016/j.apenergy.2021.116952.
B. Jana, S. Mitra, and S. Acharyya, “Repository and Mutation based Particle Swarm Optimization (RMPSO): A new PSO variant applied to reconstruction of Gene Regulatory Network,” Applied Soft Computing Journal, vol. 74, pp. 330–355, Jan. 2019, doi: 10.1016/j.asoc.2018.09.027.
R. R. Ihsan, S. M. Almufti, B. M. S. Ormani, R. R. Asaad, and R. B. Marqas, “A Survey on Cat Swarm Optimization Algorithm,” Asian Journal of Research in Computer Science, pp. 22–32, Jun. 2021, doi: 10.9734/ajrcos/2021/v10i230237.
A. M. Ahmed, T. A. Rashid, and S. A. M. Saeed, “Cat Swarm Optimization Algorithm: A Survey and Performance Evaluation,” Computational Intelligence and Neuroscience, vol. 2020. Hindawi Limited, 2020. doi: 10.1155/2020/4854895.
L. Pappula and D. Ghosh, “Cat swarm optimization with normal mutation for fast convergence of multimodal functions,” Applied Soft Computing Journal, vol. 66, pp. 473–491, May 2018, doi: 10.1016/j.asoc.2018.02.012.
Y. Meraihi, A. B. Gabis, S. Mirjalili, and A. Ramdane-Cherif, “Grasshopper optimization algorithm: Theory, variants, and applications,” IEEE Access, vol. 9, pp. 50001–50024, 2021, doi: 10.1109/ACCESS.2021.3067597.
L. Abualigah and A. Diabat, “A comprehensive survey of the Grasshopper optimization algorithm: results, variants, and applications,” Neural Computing and Applications, vol. 32, no. 19. Springer Science and Business Media Deutschland GmbH, pp. 15533–15556, Oct. 01, 2020. doi: 10.1007/s00521-020-04789-8.
S. Zhao, P. Wang, A. A. Heidari, X. Zhao, C. Ma, and H. Chen, “An enhanced Cauchy mutation grasshopper optimization with trigonometric substitution: engineering design and feature selection,” Eng Comput, Dec. 2021, doi: 10.1007/s00366-021-01448-x.
S. A. A. Ghaleb, M. Mohamad, E. F. H. Syed Abdullah, and W. A. H. M. Ghanem, “Integrating mutation operator into grasshopper optimization algorithm for global optimization,” Soft comput, vol. 25, no. 13, pp. 8281–8324, Jul. 2021, doi: 10.1007/s00500-021-05752-y.
B. N. Silva and K. Han, “Mutation operator integrated ant colony optimization based domestic appliance scheduling for lucrative demand side management,” Future Generation Computer Systems, vol. 100, pp. 557–568, 2019, doi: 10.1016/j.future.2019.05.052.
J. H. Tam, Z. C. Ong, Z. Ismail, B. C. Ang, and S. Y. Khoo, “A new hybrid GA−ACO−PSO algorithm for solving various engineering design problems,” Int J Comput Math, vol. 96, no. 5, pp. 883–919, 2019, doi: 10.1080/00207160.2018.1463438.
D. M. Chitty, “Partial-ACO as a GA mutation operator applied to TSP instances,” GECCO 2021 Companion - Proceedings of the 2021 Genetic and Evolutionary Computation Conference Companion, pp. 69–70, 2021, doi: 10.1145/3449726.3459424.
H. Li and W. Li, “Enhanced artificial bee Colony algorithm and its application in multi-threshold image feature retrieval,” Multimed Tools Appl, vol. 78, no. 7, pp. 8683–8698, 2019, doi: 10.1007/s11042-018-6066-6.
F. Ye, Z. Zhou, H. Tian, Q. Sun, Y. Li, and T. Jiang, “Intelligent Anti-Jamming Decision Method Based on the Mutation Search Artificial Bee Colony Algorithm for Wireless Systems,” 2019 USNC-URSI Radio Science Meeting (Joint with AP-S Symposium), USNC-URSI 2019 - Proceedings, pp. 27–28, 2019, doi: 10.1109/USNC-URSI.2019.8861785.
G. E. Yuliastuti, A. M. Rizki, W. F. Mahmudy, and I. P. Tama, “Optimization of Multi-Product Aggregate Production Planning using Hybrid Simulated Annealing and Adaptive Genetic Algorithm,” International Journal of Advanced Computer Science and Applications, vol. 10, no. 11, pp. 484–489, 2019, doi: 10.14569/IJACSA.2019.0101167.
B. Jana, S. Mitra, and S. Acharyya, “Repository and Mutation based Particle Swarm Optimization (RMPSO): A new PSO variant applied to reconstruction of Gene Regulatory Network,” Applied Soft Computing Journal, vol. 74, pp. 330–355, Jan. 2019, doi: 10.1016/j.asoc.2018.09.027
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