Search algorithms are a set of techniques used to find a specific item or a group of items from a collection of data. They are used in a wide range of fields such as computer science, artificial intelligence, and operations research. Some common types of search algorithms include:

**Breadth-first search (BFS)**: This algorithm explores all the vertices of a graph or all the nodes of a tree in breadth-first order.**Depth-first search (DFS)**: This algorithm explores all the vertices of a graph or all the nodes of a tree in depth-first order.**Dijkstra’s algorithm**: This algorithm is used for finding the shortest path between two nodes in a graph with non-negative edge weights.**A* (A-star) algorithm**: This algorithm is used for finding the shortest path between two nodes in a graph, it’s an extension of Dijkstra’s algorithm that uses a heuristic function to guide the search.**Best-first search**: This algorithm explores the graph by expanding the node that is closest to the goal, as determined by a heuristic function.**Hill Climbing**: This algorithm is a type of local search algorithm that starts with an arbitrary solution and then iteratively improves it by making small changes to the solution.**Beam Search**: This algorithm is a heuristic search algorithm that explores a limited set of the most promising nodes at each level.**Genetic Algorithm**: This algorithm is a search heuristic that is inspired by the process of natural selection.**Simulated Annealing**: This algorithm is a probabilistic technique for approximating the global optimum of a given function.**Tabu Search**: This algorithm is a metaheuristic search method that uses memory-based strategies to avoid getting stuck in local optima.

These are some of the most popular search algorithms, each of them has its own use case and characteristics, and it’s good to have a general understanding of them.

Here is a simple Python implementation of some of the search algorithms mentioned:

## Breadth-first search (BFS)

```
from collections import deque
def bfs(graph, start):
visited = set()
queue = deque([start])
while queue:
vertex = queue.popleft()
if vertex not in visited:
visited.add(vertex)
queue.extend(graph[vertex] - visited)
return visited
```

## Depth-first search (DFS)

```
def dfs(graph, start, visited=None):
if visited is None:
visited = set()
visited.add(start)
for next in graph[start] - visited:
dfs(graph, next, visited)
return visited
```

## Dijkstra’s algorithm

```
import heapq
def dijkstra(graph, start):
heap = [(0, start)]
visited = {}
while heap:
(cost, vertex) = heapq.heappop(heap)
if vertex not in visited:
visited[vertex] = cost
for neighbor, weight in graph[vertex].items():
if neighbor not in visited:
heapq.heappush(heap, (cost + weight, neighbor))
return visited
```

## A* (A-star) algorithm

```
import heapq
def a_star(graph, start, goal):
heap = [(0, start)]
visited = {}
while heap:
(cost, vertex) = heapq.heappop(heap)
if vertex == goal:
return visited
if vertex not in visited:
visited[vertex] = cost
for neighbor, weight in graph[vertex].items():
if neighbor not in visited:
heapq.heappush(heap, (cost + weight + heuristic(neighbor, goal), neighbor))
return visited
```

It’s worth noting that these are simple implementation examples, and in real-world scenarios, the graph data structure, the heuristic function and the termination condition of the algorithm might be different. Also, some algorithms are not implemented here due to their complexity, but the basic idea is the same and it can be used as a starting point to implement them.