# A star search algorithm in matlab

The following Matlab project contains the source code and Matlab examples used for a star search algorithm.
MATLAB/C++ mixed implementation for Astar search algorithm
Usage:
1.

The following Matlab project contains the source code and Matlab examples used for a* (a star) search for path planning tutorial.
The A* search algorithm is a simple and effective technique that can be used to compute the shortest path to a target location. This tutorial presents a detailed description of the algorithm and an interactive demo.

The following Matlab project contains the source code and Matlab examples used for m sequence generation program using a search algorithm.
Maximum length sequence assuming distinct
values = baseVal^powerVal-1
[ms]=mseqSearch(powerVal,baseVal)
OUTPUT:
ms: generated maximum length sequence, of length
basisVal^powerVal-1 such that all values occur
with equal frequency=basisVal except zero,
which occurs basisVal-1 times
INPUT:
baseVal: any prime number up to 31

The following Matlab project contains the source code and Matlab examples used for findnearest algorithm.
This is a search algorithm but what makes it different is that it checks if the data you are looking for in the data matrix.

The following Matlab project contains the source code and Matlab examples used for an implementation of a star search algorithm for grid search.
A Star search algorithm is used for finding optimized path from a start to goal state.

The following Matlab project contains the source code and Matlab examples used for binary gravitational search algorithm (bgsa).
Here is the Mathlab code for 'Binary Gravitational search algorithm'.

**Simulated annealing (SA)** is a generic probabilistic metaheuristic for the global optimization problem of locating a good approximation to the global optimum of a given function in a large search space. It is often used when the search space is discrete (e.g., all tours that visit a given set of cities).

Particle swarm optimization (PSO) is a computational method that optimizes a problem by iteratively trying to improve a candidate solution with regard to a given measure of quality. PSO optimizes a problem by having a population of candidate solutions, here dubbed particles, and moving these particles around in the search-space according to simple mathematical formulae over the particle's position and velocity.