The roulette wheel selection is a critical process in heuristic algorithms, enabling the probabilistic choice of items based on assigned fitness values. It selects an item with a probability proportional to its fitness value. This technique is commonly employed in ant-colony algorithms to randomly determine the next city to visit when solving the traveling salesman problem. Our study focuses on parallel algorithms designed to select one of multiple processors, each associated with fitness values, using random wheel selection. We propose a novel approach called logarithmic random bidding, which achieves an expected runtime logarithmic to the number of processors with non-zero fitness values, using the CRCW-PRAM model with a shared memory of constant size. Notably, the logarithmic random bidding technique demonstrates efficient performance, particularly in scenarios where only a few processors are assigned non-zero fitness values.

翻译：轮盘赌选择是启发式算法中的关键步骤，它根据适应度值对项目进行概率选择。该方法按与适应度值成正比的概率选取项目，常用于蚁群算法中求解旅行商问题时随机确定下一访问城市。本研究聚焦于并行算法设计，旨在通过随机轮盘选择从多个关联适应度值的处理器中选取其一。我们提出一种名为"对数随机竞标"的新方法，该方法基于常数大小共享内存的CRCW-PRAM模型，实现了与具有非零适应度值的处理器数量呈对数关系的期望运行时间。值得关注的是，该对数随机竞标技术尤其适用于仅有少量处理器被分配非零适应度值的场景，展现出优异的性能表现。