In the worst-case scenario, the number of swaps performed by selection sort is O(n^2). This is because, in each iteration, the algorithm selects the minimum element and swaps it with the element at the beginning, contributing to a quadratic number of swaps for a sorted array.

Greedy algorithms may not always provide the optimal solution for the Knapsack Problem. While they make locally optimal choices at each step, the overall result may not be the most optimal solution.

Dijkstra's algorithm is more efficient than Bellman-Ford algorithm for finding the shortest path in a graph with non-negative edge weights. Dijkstra's algorithm has a time complexity of O((V + E) log V), where V is the number of vertices and E is the number of edges.

A queue is commonly used in BFS to keep track of visited vertices. The queue follows the First In First Out (FIFO) principle, ensuring that vertices are processed in the order they are discovered.

Manacher's Algorithm is known for its linear-time complexity and constant space usage. It processes the string in a way that avoids redundant computations, making it an efficient solution for finding the Longest Palindromic Substring.

"FIFO" stands for "First In, First Out" in the context of a queue. It means that the first element added to the queue will be the first one to be removed. This ensures that the order of elements in the queue is preserved, and elements are processed in the order they are received.

A* search may not be the most suitable approach for optimizing task scheduling in a project management application. While it excels in pathfinding, other scheduling algorithms may be more appropriate for managing complex dependencies and resource constraints in project scheduling.

In each pass through the array in bubble sort, adjacent elements are compared, and if they are in the wrong order, they are swapped. This process continues until the entire array is sorted. As a result, the largest unsorted element "bubbles" to its correct position in each pass. Bubble sort repeats this process for each element in the array until no swaps are needed, indicating that the array is sorted. The algorithm's name is derived from this bubbling behavior that occurs during sorting.

The greedy approach makes locally optimal choices at each step, hoping to find a global optimum. Dynamic programming, however, systematically solves subproblems and builds up to the overall optimal solution.

Dynamic programming contributes to solving the Knapsack Problem efficiently by breaking down the problem into smaller subproblems, storing the solutions to these subproblems, and eliminating redundant calculations. This approach enables the computation of optimal solutions in polynomial time.

In Matrix Chain Multiplication, the order of matrix multiplication is significant because it affects the associativity of the operation. Different parenthesizations may result in different numbers of scalar multiplications, and the algorithm aims to find the optimal parenthesization to minimize computational cost.

The time complexity of the standard dynamic programming approach for Matrix Chain Multiplication is O(n^3), where 'n' is the number of matrices being multiplied. This is achieved through the dynamic programming technique of solving subproblems and storing their solutions in a table for reuse.