In ant colony optimization algorithm (ACO), the pick_move function is used to determine the next movement of ants. This function calculates the probability of movement and samples the next city based on pheromone concentration and heuristic information, combined with the mask of the visited city.

The function should optimize the following points:
1. Balance the weights of pheromone and heuristic information (alpha and beta parameters)
2. Always include `from torch.distributions import Categorical` when using the Categorical distribution for sampling. This is critical - failure to include this import will cause runtime errors.
3. Implement individualized decision strategies for each ant to improve solution diversity
4. Use tensor-based operations to efficiently apply different sampling strategies per ant
5. Additional heuristic rules may be introduced to guide ants to make better decisions

The function takes the following parameters:
- prev: A tensor of shape (n_ants,) representing the current position of all ants
- mask: Boolean tensor of shape (n_ants, p_size), marked with 0 for visited cities
- require_prob: Boolean value indicating whether logarithmic probability needs to be returned
- pheromone: The pheromone matrix with shape (p_size, p_size)
- heuristic: The heuristic information matrix with shape (p_size, p_size)
- alpha: Parameter controlling the influence of pheromone trails
- beta: Parameter controlling the influence of heuristic information

Function returns:
- actions: Tensor with shape (n_ants,) representing the next action chosen by ants
- log_probs: If require_prob is True, return a tensor with shape (n_ants,) representing the logarithmic probability of the actions

Note: When implementing per-ant decision strategies (like epsilon-greedy), use tensor operations such as 'torch.rand(n_ants)' to generate random values for all ants simultaneously rather than scalar operations.

