The Knuth-Morris-Pratt (KMP) algorithm utilizes a failure function (also known as the longest prefix suffix array) to optimize the search process. The failure function is precomputed based on the pattern and helps the algorithm determine the maximum length of a proper suffix that matches a proper prefix within the pattern. This information is then used to efficiently skip unnecessary comparisons during the search.

Metacharacters in regular expressions are special characters that provide a specific meaning to a search pattern. They allow for more flexible and powerful matching by representing concepts like repetition, alternatives, and grouping in the pattern.

In this scenario, Manacher's Algorithm would be the preferred choice. It has a linear time complexity and is specifically designed for finding the Longest Palindromic Substring efficiently, making it suitable for large strings.

Computational challenges include dealing with integer overflow, handling precision issues with floating-point arithmetic, and optimizing recursive approaches. Strategies may involve memoization to store and reuse previously computed results, optimizing algorithms for better efficiency, and considering alternative data types for large values of n.

The primary purpose of Dijkstra's algorithm is to find the shortest path between two nodes in a graph, particularly in a graph with non-negative edge weights. It is commonly used in routing and network protocols.

Linear search performs better on sorted arrays. This is because, in a sorted array, once a value greater than the target is encountered, the search can stop, resulting in early termination. On the other hand, in an unsorted array, the search continues until the target is found or the entire array is traversed.

BFS explores all nodes at the same level before moving to the next level. This ensures that the algorithm covers all nodes at a particular level before proceeding to the subsequent level in a graph traversal.

The worst-case time complexity of the selection sort algorithm is O(n^2), where 'n' is the number of elements in the array. This is due to the nested loops used to find the minimum element in each iteration.

In a scenario with an unsorted list that is constantly changing, linear search has the advantage of simplicity. However, its time complexity of O(n) may lead to inefficiency as the list size grows. Advantages include ease of implementation, but disadvantages involve potentially slower performance compared to other algorithms like hashing or jump search, which can exploit certain characteristics of the data for faster retrieval.

It ensures finding the shortest path by maintaining a priority queue that contains the shortest distance to each node from the source. The priority queue helps prioritize nodes based on their distance values, facilitating efficient path exploration.