Added Robin Hood Hashing in Hashmap for Optimized Probe Length Management

[divyalakshmi0]

Title: Add Robin Hood Hashing Algorithm for Optimized Hash Table Collisions

Issue: #1462

Description:
This PR introduces an implementation of the Robin Hood Hashing Algorithm, a variation of open addressing designed to reduce clustering and improve the lookup and insertion efficiency in hash tables. Robin Hood hashing balances load by minimizing the maximum probe distance for each entry, ensuring more efficient performance even at high load factors.

Changes:

• Added robin_hood_hashing.c implementing the Robin Hood Hashing Algorithm in C.
• Functions included:
  • insert: Inserts key-value pairs with probe distance adjustments.
  • search: Searches for keys with minimized probe distances.
  • delete: Marks elements as deleted to preserve probe order.
• Added test cases for insertion, search, and deletion to validate the functionality and efficiency of the implementation.

Motivation:
Robin Hood Hashing reduces clustering in hash tables by balancing probe distances, making it useful for handling high load factors efficiently. This feature will help users working with dense datasets and scenarios requiring fast lookups.

How it helps:

• Efficient Access: Reduces maximum probe length, improving average lookup time.
• Improved Load Balancing: Decreases clustering, maintaining stable access times even with a high load factor.
• Optimal Collision Handling: Ensures minimal probe distance through dynamic insertion ordering.

Testing:

• Basic insertion, search, and deletion cases with different key-value pairs.
• Edge cases including collisions and deletion of existing elements.

Additional Notes:
This implementation adds value for users who handle large datasets in hash tables and require optimal collision resolution.