Abstract
Symbiotic Organism Search (SOS) algorithm is highly praised by researchers for its excellent convergence performance, global optimization ability and simplicity in solving various continuous practical problems. However, in the real world, there are many binary problems, which can only take values of 0 and 1, that still need to be solved. Since the original SOS algorithm cannot directly solve the binary problem, the original ASOS Binary SOS (BSOS) algorithm has the disadvantage of premature convergence. In order to improve the limitations of the ASBSOS algorithm, we propose an Improved BSOS (IBSOS) algorithm. As we all know, the transfer function is very important in the binarization of continuous optimization algorithms. Therefore, we used 9 transfer functions in the IBSOS algorithm to binarize the continuous SOS algorithm and analyzed the impact of each transfer function on the performance of the BSOS algorithm. Moreover, we use the same three biological symbiosis strategies as the continuous SOS algorithm in our proposed IBSOS algorithm to binarize the SOS algorithm to improve The diversity of the algorithm execution process and the ability to balance algorithm exploration and development. In order to verify the performance of IBSOS using different transfer functions, we use 13 benchmark functions to show the global optimization capability and convergence speed of the BSOS algorithm. Finally, we apply the algorithm to feature selection in the ten data sets of UCI. The experimental results with low classification error and few features further verify the excellent performance of the IBSOS algorithm.
Highlights
I N today’s huge information systems, thousands of programs are constantly generating a large number of datasets
Both are completed in the same optimization process, which means the feature selection is automatically performed during the model training process
Where XiG and XjG represent the state of the virus and the human host, Xparasite represents the state of the intermediate host, and Bmax and Bmin represent the range of the biological activity interval, respectively
Summary
I N today’s huge information systems, thousands of programs are constantly generating a large number of datasets. SOS ALGORITHM we introduce the original SOS algorithm, which is inspired by the three coexistence relations of Mutualism, Commensalism, and Parasitism among organisms in the ecosystem. B2 = round(1 + rand(0, 1)), Where XGi and XGj respectively represent the position states of the i-th creature and the i-th creature when iterating to the G-th generation They update their positions to Xi_new and Xj_new according to the symbiotic relationship between organisms, and maintain themselves to be optimal through eq.. Where XiG and XjG represent the state of the virus and the human host, Xparasite represents the state of the intermediate host, and Bmax and Bmin represent the range of the biological activity interval, respectively The creatures update their positions through Mutualism, Commensalism, and Parasitism until the maximum number of iterations is reached. The SOS algorithm pseudo-code is shown in Algorithm 1
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