Abstract
The article describes a co-evolutionary self-tuning algorithm for solving global optimization problems. The algorithm simulates the selfish behavior of herbivores attacked by a herd of predators. Search agents are controlled by a set of attractive search operators based on patterns of individual and collective behavior of agents, as well as mechanisms of population selection in the “prey-predator” system. Agents move in the space of solutions to the optimization problem using a set of operators imitating various types of behavior, including selfishness. The proposed co-evolutionary self-adjusting algorithm allows not only simulating multiple types of selfish behavior, unlike most competing algorithms. The algorithm includes computational mechanisms to maintain a balance between the rate of convergence of the algorithm and the diversification of the solution search space. The algorithm's performance is analyzed using a series of experiments for the problems of finding the global minimum in a set of 5 known test functions. The authors have compared the results with seven competing bioheuristics for indicators such as the average best-to-date solution, the median best-to-date solution, and the standard deviation of the current best solution. The accuracy of the proposed algorithm turned out to be higher than that of competing algorithms. Nonparametric proof of the statistical significance of the results obtained using the Wilcoxon signed-rank test allows us to assert that the results of the co-evolutionary self-tuning algorithm are statistically significant.
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Acknowledgements
The study was performed by the grant from the Russian Science Foundation № 22–21-00316, https://rscf.ru/project/22-21-00316/ in the Southern Federal University.
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Rodzin, S., Kureichik, V., Rodzina, L. (2022). Co-evolutionary Self-adjusting Optimization Algorithm Based on Patterns of Individual and Collective Behavior of Agents. In: Silhavy, R. (eds) Cybernetics Perspectives in Systems. CSOC 2022. Lecture Notes in Networks and Systems, vol 503. Springer, Cham. https://doi.org/10.1007/978-3-031-09073-8_22
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