Analysis of the Code
The provided code implements the Ant Colony Optimization (ACO) algorithm to solve the Traveling Salesman Problem (TSP). While the code captures the essential logic of ACO, there are several issues and opportunities for improvement:

Pheromone Matrix Initialization (Shallow Copy Issue):

The pheromone matrix is initialized as [[1.0] * cities_num] * cities_num. This leads to all rows being shallow copies of each other. Any update to one row will reflect in all rows.
Randomness in City Selection:

The random.choices function in city_select is used to select the next city based on probability weights. However, randomness can sometimes lead to inconsistent solutions, and there’s no seed to ensure reproducibility of results.
Unnecessary Deep Copy of Cities:

The copy.deepcopy(cities) is used to create a deep copy of the cities dictionary for each ant. This is computationally expensive and unnecessary. Instead, working directly with a list of remaining city indices would be more efficient.
Code Readability & Modularity:

Some parts of the code can be simplified for better readability. The use of next(iter(...)) to extract the first element of a dictionary in multiple places reduces clarity.
Boundary Handling (Empty Input Check):

The code does not handle the case when no cities are provided (i.e., cities={}). This results in a StopIteration error in city_select. An explicit check for empty input at the start of the main function would help.
Docstrings and Type Hints:

The type hints in functions are clear, but some functions lack docstrings explaining their behavior (e.g., pheromone_update, city_select). Providing more detailed explanations for each function would improve maintainability.
Reusability of Results:

The current approach recalculates distances between cities multiple times. Precomputing the distance matrix once at the start would improve performance.

Analysis of the Code
Type Hinting:

The use of type hints is generally good. However, list[str] and dict without explicit type definitions could lead to confusion. Using List and Dict from the typing module would be clearer and more consistent.
State Representation:

The states in adlist are represented as dictionaries. This works but could be less efficient in terms of memory usage and performance compared to a dedicated class for state representation.
Function Naming:

The function find_next_state does a lookup but could benefit from being renamed to reflect that it’s finding a state by character, e.g., find_state_by_character.
Output Handling:

The output accumulation in the set_fail_transitions method uses list concatenation which can be inefficient. Instead, using extend() would be better for performance.
Loop Conditions:

The loop conditions that involve checking for None states are repetitive. This could be refactored to simplify the logic.
Docstring:

The docstring for the search_in method could be enhanced to explain the output format in more detail.
Main Check:

The if __name__ == "__main__": section is good, but it might be beneficial to add a simple example or usage guide for clarity.

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Analysis of the Code
Type Hinting:

The function lacks type hints for the parameters and return type. Adding them improves readability and helps with static type checking.
Variable Naming:

The variable names such as abs_length are somewhat misleading. A name like max_length would more accurately describe its purpose.
Output List Initialization:

The use of list for output_list is good, but the name could be more descriptive, such as result_chars.
Loop Condition:

The loop iterates based on the maximum length of the two strings, which is correct, but using a single loop for the two strings may make the code clearer and more efficient.
Docstring:

The docstring is clear but could use additional details on the behavior when one string is shorter than the other.

for more information, see https://pre-commit.ci