Algorithmic paradigms are:

General approaches to the construction of efficient solutions to problems

Any basic, commonly used approach in designing algorithms could be considered an algorithmic paradigm:

Divide and Conquer

Idea: Divide problem instance into smaller sub-instances of the same problem, solve these recursively, and then put solutions together to a solution of the given instance.

Examples: Mergesort, Quicksort, Strassen’s algorithm, FFT.

Greedy Algorithms

Idea: Find solution by always making the choice that looks optimal at the moment — don’t look ahead, never go back.

Examples: Prim’s algorithm, Kruskal’s algorithm.

Dynamic Programming

Idea: Turn recursion upside down.

Example: Floyd-Warshall algorithm for the all pairs shortest path problem.

The word paradigm does translate to example, but that’s not how it’s used in a scientific context. Your examples are all examples of algorithms (except the travelling salesman problem, which is a NP-hard problem), none of which is trivial enough to be considered an algorithmic paradigm.

Common design Algorithmic Paradigms:

• Divide and conquer : Recursively breaking down a problem into two or more sub-problems of the same (or related) type.
• Dynamic programming : breaking it down into a collection of simpler subproblems. Example: Tower of Hanoi puzzle
• Greedy algorithm : the problem solving heuristic of making the locally optimal choice at each stage. Example: traveling salesman problem
• Backtracking : is a general algorithm for finding all (or some) solutions to some computational problems Example: Sudoku puzzle solved by backtracking.
• Brute Force : a very general problem-solving technique that consists of systematically enumerating all possible candidates for the solution and checking whether each candidate satisfies the problem’s statement.

You can find number of example on geeksforgeeks