Set state variables and determine value ranges

Determine the state group and list the initial state set and target state set respectively.

Define and determine action sets

Estimate the number of all state spaces, and list or describe all state spaces if possible

When the number of states is not very large, draw a state space diagram according to the ordered tuples of the problem, and search and solve according to the state space diagram.

completeness

Optimality

time complexity

Space scum degree

The core idea is to select the local optimal solution every time, but this algorithm does not guarantee that the final result is the global optimal solution.

Most greedy algorithms find optimal paths based on graphs

A typical case of greedy algorithm is shortest path search.

Similar to the breadth-first search algorithm, the only difference is that it traverses the nodes of the number along the depth of the tree, traversing the branches of the search number as much as possible

It will first traverse the first child node of the root node, then traverse the first child node of the child node, and continue traversing along the depth of the tree.

One of the algorithms to solve the automatic path finding function

As a common graph search algorithm, it is also widely used to solve various other algorithmic problems.

Generally, for a node, the set of its neighbor nodes is called the open list, and before this node is traversed, all other nodes that have been traversed are stored in the close list.

Completeness: When the branching factor is limited, the algorithm is complete

Optimality: This algorithm is optimal when the path cost is a non-decreasing function of the node depth

Time complexity: consistent with breadth-first search algorithm

space complexity

Two of the fundamental goals of computer science are to discover algorithms that can be proven to perform well and lead to optimal or suboptimal solutions, but heuristic algorithms attempt to provide one or all of the goals at once.

Hill-climbing method The hill-climbing method is a completely greedy algorithm. The optimal position is selected at each step, and it may only be able to obtain a local optimal solution.

Randomly restart the mountain climbing method. When the number of climbing steps exceeds a certain value, the chessboard will be disrupted again and the mountain will be "climbed" again.

The average path length of the steepest hill climbing method is shorter, but the search cost is more than the preferred hill climbing method. The reason is that in order to get the steepest child node, all adjacent child nodes need to be explored.

Try to expand the node closest to the goal, on the grounds that it may lead to a solution quickly

To solve the LRP problem, the convergence criterion is in a computational sense

The most basic method of solving integer programming

The search efficiency of algorithm A depends largely on the valuation function h(n)

Under the premise of satisfying **h(n)≤h(n)**, the larger the value of h(n), the better.

Informational: The larger the value of h(n), the more heuristic information it carries, the fewer nodes the A algorithm will expand when searching, and the higher the search efficiency.

For function optimization problems that were difficult to solve in the past, complex multi-objective programming problems, piping and wiring problems in industrial and agricultural production, as well as machine learning, image recognition, weight coefficient adjustment and network construction of artificial neural networks, etc.

It is a probabilistic technique used to find optimized paths in graphs.

It is inspired by the behavior of ants finding paths in the process of searching for food. The ant colony algorithm is a simulated evolutionary algorithm. Preliminary research shows that the algorithm has many excellent properties.

It is a stochastic optimization algorithm based on Monte Carlo iterative solution strategy. Its starting point is based on the similarity between the annealing process of solid substances in physics and general combinatorial optimization problems.

Its purpose is to provide an effective approximate solution algorithm for problems with NP (Non-deterministic Polynomial) complexity. It overcomes the shortcomings of other optimization processes that are prone to falling into local minima and dependence on initial values.

It has attracted the attention of the academic community due to its advantages such as easy implementation, high accuracy, and fast convergence, and has demonstrated its superiority in solving practical problems.

It is a search method used to jump out of the local optimum.

TS is a manifestation of artificial intelligence and an extension of local domain search.

Tabu search is a sub-heuristic random search algorithm. It starts from an initial feasible solution, selects a series of specific search directions (movements) as trials, and selects the movement that changes the value of a specific objective function the most.

In order to avoid falling into the local optimal solution, a flexible "memory" technology is used in TS search to record and select the optimization process that has been carried out to guide the next search direction.