Inspired by the biological behavior of domestic cats, the Cat Swarm Optimization (CSO) is a metaheuristic which has been successfully applied to solve several optimization problems. For binary problems, the Boolean Binary Cat Swarm Optimization (BBCSO) presents consistent performance and differentiates itself from most of the other algorithms by not considering the agents as continuous vectors using transfer and discretization functions. In this paper, we present a simplified version of the BBCSO. This new version, named Simplified Binary CSO (SBCSO) which features a new position update rule for the tracing mode, demonstrates improved performance, and reduced computational cost when compared to previous CSO versions, including the BBCSO. Furthermore, the results of the experiments indicate that SBCSO can outperform other well-known algorithms such as the Improved Binary Fish School Search (IBFSS), the Binary Artificial Bee Colony (BABC), the Binary Genetic Algorithm (BGA), and the Modified Binary Particle Swarm Optimization (MBPSO) in several instances of the One Max, 0/1 Knapsack, Multiple 0/1 Knapsack, SubsetSum problem besides Feature Selection problems for eight datasets.

中文翻译：

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简化的二进制猫群优化

受家猫的生物学行为启发，猫群优化（CSO）是一种元启发式方法，已成功应用于解决多个优化问题。对于二进制问题，布尔二进制猫群优化（BBCSO）表现出一致的性能，并且通过不将代理视为使用传递和离散化函数的连续向量，从而使其与大多数其他算法区分开来。在本文中，我们提出了BBCSO的简化版本。与以前的CSO版本（包括BBCSO）相比，此新版本名为Simplified Binary CSO（SBCSO），具有跟踪模式的新位置更新规则，具有更高的性能，并降低了计算成本。此外，