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The goal of the optimization is to find the minimum point on the cost function (or as close to the minimum point as possible, given a reasonable amount of time).Ĭonsider the following example of traffic minimization. It can be easy to get stuck in a local optimum.

The set of all possible configurations and their costs form the search space.īecause there are so many possible configurations to choose from, it is sometimes difficult to identify the best solution, particularly when the problem space is very large. A particular arrangement of the variables is called the configuration of the problem, and to each configuration there is a cost associated. Usually, an optimization problem involves a lot of variables that can interact in many ways to influence the final cost.

Walkers can be used in various ways: for example, you may choose to have many walkers begin from the same starting point, or have them begin from different locations, and so on. Often, this will be referred to as a walker. You can imagine a person or a particle in the search space, and each step taken creates a path, or walk, through the optimization landscape. The lowest point, which corresponds to the lowest cost solution, is called the global minimum. Each point in this search space is a valid solution to the problem.

The search space contains all the feasible solutions to an optimization problem. For more information, see Cost functions. It is a mathematical function to be minimized. The cost function is the way that the cost varies as a function of the system configuration. To understand optimization problems, you first need to learn some basic terms and concepts.