Update ant_colony_optimization_algorithms.py

[akshitbansal2005]

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.

Describe your change:

• Add an algorithm?
• Fix a bug or typo in an existing algorithm?
• Add or change doctests? -- Note: Please avoid changing both code and tests in a single pull request.
• Documentation change?

Checklist:

• I have read CONTRIBUTING.md.
• This pull request is all my own work -- I have not plagiarized.
• I know that pull requests will not be merged if they fail the automated tests.
• This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
• All new Python files are placed inside an existing directory.
• All filenames are in all lowercase characters with no spaces or dashes.
• All functions and variable names follow Python naming conventions.
• All function parameters and return values are annotated with Python type hints.
• All functions have doctests that pass the automated testing.
• All new algorithms include at least one URL that points to Wikipedia or another similar explanation.
• If this pull request resolves one or more open issues then the description above includes the issue number(s) with a closing keyword: "Fixes #ISSUE-NUMBER".

[algorithms-keeper]

Closing this pull request as invalid

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